Audio Classification from Musical Instruments¶

by Abhinaba Ghose MSCA 31009 2¶

1. Exploratory Data Analysis¶

# Import libraries
import pandas as pd
import pickle
import shutil, os
import matplotlib.pyplot as plt
from IPython.display import Image
from tqdm import tnrange, tqdm_notebook
import wave
from scipy.io import wavfile
import seaborn as sns
import numpy as np
from python_speech_features import mfcc, logfbank
from tqdm  import tqdm
import librosa
from sklearn.metrics import accuracy_score
from keras.backend.tensorflow_backend import set_session
from keras.callbacks import ModelCheckpoint

from keras.layers import Conv2D, MaxPool2D, Flatten, LSTM
from keras.layers import Dropout, Dense, TimeDistributed, Input, BatchNormalization, GlobalAveragePooling2D, concatenate
from keras.models import Sequential, load_model
from keras.utils import to_categorical
from sklearn.utils.class_weight import compute_class_weight
import tensorflow as tf
from keras.applications import MobileNetV2
from keras.regularizers import l1, l2

train_path = './freesound-audio-tagging/audio_train/'
df = pd.read_csv('./freesound-audio-tagging/train.csv')

df.head()

df.groupby('label').count().head()

Select Instrument Labels

select_labels = ['Acoustic_guitar', 'Bass_drum','Cello','Clarinet','Double_bass','Flute','Hi-hat','Saxophone','Snare_drum','Violin_or_fiddle']

# Here we get a count of all labels
df_select = df[df['label'].isin(select_labels)]
df_select.groupby('label').count()

# We take 2/3 of all the samples to train

df1 = pd.DataFrame()
for c in select_labels:
    df_stage = df_select[df_select.label == c].sample(frac = 2/3, random_state =1).copy()
    df1 = df1.append(df_stage, ignore_index = True)

df1.groupby('label').count()

cat_labels = df1.groupby(by = 'label').count().index

cat_count = df1.groupby(by = 'label').count()['fname'].sort_values(ascending = False)

# Finding the total size of all files we will be working on so there is enough computing resources
size = 0
for f in df1.fname:
    b = os.path.getsize(train_path+f)
    size = size + b

print('Total size of train data: {} GB'. format(round(size/1024**3,2)))

Total size of train data: 0.98 GB

# Create a function to read a file from the folder. And then find the frames and time series of the audio

def load_wav_file(name):
    _, b = wavfile.read(train_path+name)
    assert _ == SAMPLE_RATE
    return b
SAMPLE_RATE = 44100

df1['time_series'] = df1['fname'].apply(load_wav_file)

df1['nframes'] = df1.time_series.apply(len)

df1.head()

# Plot the number of frames for those audio files

plt.figure(figsize=(12,8))
sns.distplot(df1.nframes.values, bins = 50, kde = False)
plt.xlabel('nframes', fontsize=12)
plt.title('Histogram of number of frames per audio file')
plt.show()

# Visualize how different insutrument audios look like statistically

plt.figure(figsize=(12,8))
boxplot = sns.boxplot(x = 'label', y = 'nframes', data = df1)
boxplot.set(xlabel = '', ylabel='')
plt.title('Distribution of audio frames per label', fontsize = 17)
plt.xticks(rotation = 80, fontsize = 17)
plt.yticks(fontsize = 17)
plt.xlabel('Label name', fontsize = 17)
plt.ylabel('nframes', fontsize = 17)
plt.show()

Plotting audio files based on audio features such as signals, FFT, Fourier Bank filters¶

def plot_signals(signals):
    fig, axes = plt.subplots(nrows = 2, ncols = 5, sharex = False, 
                            sharey = True, figsize = (20,5))
    fig.suptitle('Time Series', size = 16)
    i =0
    for x in range(2):
        for y in range(5):
            axes[x,y].set_title(list(signals.keys())[i])
            axes[x,y].plot(list(signals.values())[i])
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i+=1

def plot_fft(fft):
    fig,axes = plt.subplots(nrows =2, ncols =5, sharex = False,
                           sharey = True, figsize=(20,5))
    fig.suptitle('Fourier Transforms', size = 16)
    i=0
    for x in range(2):
        for y in range(5):
            data = list(fft.values())[i]
            Y, freq = data[0], data[1]
            axes[x,y].set_title(list(fft.keys())[i])
            axes[x,y].plot(freq,Y)
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i+=1

def plot_fbank(fbank):
    fig,axes = plt.subplots(nrows = 2, ncols =5, sharex = False,
                           sharey = True, figsize = (20,5))
    fig.suptitle('Filter Bank Coefficients', size = 16)
    i=0
    for x in range(2):
        for y in range(5):
            axes[x,y].set_title(list(fbank.keys())[i])
            axes[x,y].imshow(list(fbank.values())[i], cmap='hot',interpolation = 'nearest')
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i+=1

def plot_mfccs(mfccs):
    fig, axes = plt.subplots(nrows = 2, ncols = 5, sharex = False, 
                            sharey = True, figsize=(20,5))
    fig.suptitle('Mel Frequency Cepstrum Coefficients', size = 16)
    i=0
    for x in range(2):
        for y in range(5):
            axes[x,y].set_title(list(mfccs.keys())[i])
            axes[x,y].imshow(list(mfccs.values())[i], cmap = 'hot',interpolation = 'nearest')
            axes[x,y].get_xaxis().set_visible(False)
            axes[x,y].get_yaxis().set_visible(False)
            i+=1

def envelope(y, rate, threshold):
    mask = []
    y = pd.Series(y).apply(np.abs) # Numpy array to series
    y_mean = y.rolling(window = int(rate/10), min_periods=1,
                      center = True).mean() # Rolling window over data 
    for mean in y_mean:
        if mean > threshold:
            mask.append(True)
        else:
            mask.append(False)
    return mask

def calc_fft(y, rate):
    n = len(y)
    freq = np.fft.rfftfreq(n, d = 1/rate)
    Y = abs(np.fft.rfft(y)/n) # Normalize length of signal #Y - magnitude
    return(Y, freq)

df2 = df1.drop(['time_series','nframes'], axis =1)
df2.set_index('fname', inplace = True)
df2.head()

# Find the length of the signal
for f in df2.index:
    rate, signal = wavfile.read(train_path+f)
    df2.at[f,'length'] = signal.shape[0]/rate

df2.head()

# Plot the average length of the signal for each label

classes = list(np.unique(df2.label))
class_dist = df2.groupby(['label'])['length'].mean()

fig, ax = plt.subplots()
ax.set_title('Class Distribution', y = 1.08)
ax.pie(class_dist, labels = class_dist.index, autopct = '%1.1f%%',
      shadow = False, startangle = 90)
ax.axis('equal')
plt.show()
#df2.reset_index(inplace = True)

df2.reset_index(inplace = True)

df2.head()

signals = {}
fft = {}
fbank = {}
mfccs = {}

for c in classes:
    wav_file = df2[df2.label == c].iloc[0,0]
    signal, rate = librosa.load(train_path+wav_file,sr = SAMPLE_RATE)
    #mask = envelope(signal, rate, 0.0005) #Librosa has smaller values of threshold
    #signal = signal[mask]
    signals[c] = signal
    fft[c] = calc_fft(signal, rate)
    
    bank = logfbank(signal[:rate], rate, nfilt=26, nfft=1103).T
    # 1/40 = 0.025 -> 25 ms window length
    # 44100/40 = 1103
    fbank[c] = bank
    mel = mfcc(signal[:rate], rate, numcep=13, nfilt=26, nfft = 1103).T
    mfccs[c] = mel

# Plot the raw signals

plot_signals(signals)
plt.show()
#signals

# Plot Fourier Fast Transformed signals

plot_fft(fft)
plt.show()

# Plot the filter banks from FFT

plot_fbank(fbank)
plt.show()

# Plot Mel-frequency cepstral coefficients (MFCCs) extracted from the filter banks above

plot_mfccs(mfccs)
plt.show()

Get rid of dead space, calculate envelope of the signal.¶

Image(filename='./freesound-audio-tagging/Dead Space.jpg')

if len(os.listdir('./freesound-audio-tagging/clean')) ==0:
    for f in tqdm_notebook(df_select.fname):
        signal, rate = librosa.load(train_path+f, sr= 16000) #Downsampling
        mask = envelope(signal, rate, 0.0005)
        wavfile.write(filename = './freesound-audio-tagging/clean/'+f,
                     rate = rate, data = signal[mask])

2. Modelling¶

# List of all classes

classes

['Acoustic_guitar',
 'Bass_drum',
 'Cello',
 'Clarinet',
 'Double_bass',
 'Flute',
 'Hi-hat',
 'Saxophone',
 'Snare_drum',
 'Violin_or_fiddle']

df2.set_index('fname', inplace = True)

np.random.choice(df2[df2.label =='Acoustic_guitar'].index) # test

'9d072bbc.wav'

def check_data(): # check data in pickles folder
    if os.path.isfile(config.p_path):
        print('Loading existing data for {} model'.format(config.mode))
        with open(config.p_path, 'rb') as handle:
            tmp = pickle.load(handle)
            return tmp
    else:
        return None

class Config:
    def __init__(self, mode = 'conv', nfilt = 26, nfeat = 13, nfft = 512, rate = 16000):
        self.mode = mode
        self.nfilt = nfilt
        self.nfft = nfft
        self.nfeat = nfeat
        self.rate = rate
        self.step = int(rate/10)
        self.model_path = os.path.join('./freesound-audio-tagging/models',mode+'.model')
        self.p_path = os.path.join('./freesound-audio-tagging/pickles', mode+'.p')

df2.head()

# Function to transform X, y values into the right format from raw files

def build_rand_feat():
    tmp = check_data()
    print('Dataframe shape', df2.shape)
    if tmp:
        print(tmp)
        return tmp.data[0], tmp.data[1]
    X=[]
    y=[]
    _min, _max = float('inf'), -float('inf')
    short = 0
    
    for _ in tqdm_notebook(range(n_samples)):
        rand_class = np.random.choice(class_dist.index, p = prob_dist)
        file = np.random.choice(df2[df2.label ==rand_class].index)
        rate, wav = wavfile.read('./freesound-audio-tagging/clean/'+file)
        #print('check')
        label  = df2.at[file,'label']
        
        
        if wav.shape[0] > 1700:
            rand_index = np.random.randint(0, wav.shape[0] - config.step) 
        else:
            short+=1
            continue
        
               
        sample = wav[rand_index:rand_index+config.step]
        
        X_sample = mfcc(sample, rate, numcep = config.nfeat, nfilt = config.nfilt,nfft=config.nfft)
        
        _min = min(np.amin(X_sample), _min)
        _max = max(np.amax(X_sample), _max)
        
        # X.append(X_sample if config.mode =='conv' else X_sample.T)
        X.append(X_sample)
        y.append(classes.index(label))
    
    print('No. of small files that did not make it to train set:', short)        
    config.min = _min
    config.max = _max
    X,y = np.array(X), np.array(y)
    X = (X-_min)/(_max - _min)
    
    if config.mode == 'conv':
        X = X.reshape(X.shape[0], X.shape[1], X.shape[2],1)
    elif config.mode == 'time':
        X = X.reshape(X.shape[0], X.shape[1], X.shape[2])
    y = to_categorical(y, num_classes=10)
    config.data = (X,y)
    
    with open(config.p_path, 'wb') as handle:
        pickle.dump(config, handle, protocol=2)
        
    return X,y

# Use 10th of a sec for an audio
# Dividing into chunks
n_samples = 2* int(df2.length.sum()/0.1)
# Cumulative to 1
prob_dist = class_dist/class_dist.sum()
choices = np.random.choice(class_dist.index, p = prob_dist)

# Using a switch to switch between convolution (conv) and RNN (time) models
# Now switching to CNN

config = Config(mode='conv')

# Function to build CNN model

def get_conv_model():
    model = Sequential()
    model.add(Conv2D(16, (3,3), activation = 'relu', strides=(1,1),
                    padding='same', input_shape = input_shape)) # what is the input size?
    model.add(Conv2D(32,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(Conv2D(64,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(Conv2D(128,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(MaxPool2D((2,2))) # Stride 2?
    
    
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128, activation='relu', activity_regularizer = l2(0.001)))
    model.add(Dense(64, activation='relu',  activity_regularizer = l2(0.001)))
    model.add(Dense(10, activation='softmax'))
    model.summary()
    model.compile(loss = 'categorical_crossentropy',
                optimizer = 'adam',
                metrics = ['acc'])
    return model

# Function to build CNN model

def get_conv_model2():
    model = Sequential()
    model.add(Conv2D(16, (3,3), activation = 'relu', strides=(1,1),
                    padding='same', input_shape = input_shape)) # what is the input size?
    model.add(Conv2D(32,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(Conv2D(64,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(Conv2D(128,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    model.add(Conv2D(256,(3,3), activation = 'relu', strides=(1,1), 
                    padding='same'))
    
    model.add(Dropout(0.25))
    
    model.add(MaxPool2D((2,2))) # Stride 2?
        
    model.add(Dropout(0.25))
    model.add(Flatten())
  
    model.add(Dense(256, activation='relu'))
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(10, activation='softmax'))
    model.summary()
    model.compile(loss = 'categorical_crossentropy',
                optimizer = 'adam',
                metrics = ['acc'])
    return model

# Function to build RNN Model
def get_recurrent_model():
    #shape of data for RNN is (n, time, feat)
    model = Sequential()
    model.add(LSTM(128, return_sequences = True, input_shape = input_shape))
    model.add(LSTM(128, return_sequences = True))
    model.add(Dropout(0.5))
    model.add(TimeDistributed(Dense(64,activation='relu')))
    model.add(TimeDistributed(Dense(32,activation='relu')))
    model.add(TimeDistributed(Dense(16,activation='relu')))
    model.add(TimeDistributed(Dense(8,activation='relu')))
    model.add(Flatten())
    model.add(Dense(10, activation = 'softmax'))
    model.summary()
    model.compile(loss = 'categorical_crossentropy',
                optimizer = 'adam',
                metrics = ['acc'])
    return model

# Use pre-trained model from MobileNetv2

def get_conv_model_MobileNetv2():
    
    input_shape_a = np.asarray(input_shape)
    input_shape_t = tf.convert_to_tensor(input_shape_a)
    print(input_shape_t)
       
    #model = Sequential()
    inp = Input(shape=(9, 13, 1))
    x = BatchNormalization()(inp) 
    
    #model = Input(input_shape_t)
    x = Conv2D(16, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = Conv2D(96, kernel_size=(3,3), padding='same', activation='relu')(x)
    #x = Conv2D(63, kernel_size=(3,3), padding='same', activation='relu')(x)
    #x = Conv2D(150, kernel_size=(3,3), padding='same', activation='relu')(x)
#     #model.add(Conv2D(16, (3,3), activation = 'relu', strides=(1,1),
#      #               padding='same', input_shape = inp )) # what is the input size?
#     model.add(Conv2D(32,(3,3), activation = 'relu', strides=(1,1), 
#                     padding='same'))
    
#     model.add(Conv2D(64,(3,3), activation = 'relu', strides=(1,1), 
#                     padding='same'))
#     model.add(Conv2D(128,(3,3), activation = 'relu', strides=(1,1), 
#                     padding='same'))
    
    # Adding MobileNetv2 architecture
    mn = MobileNetV2(include_top = False)
    mn.layers.pop(0)
    mn_out = mn(x)
    model = GlobalAveragePooling2D()(mn_out)
    model = Dense(160, activation='relu')
    model = BatchNormalization()(model)
    model = Dense(64, activation='relu')
    model = BatchNormalization()(model)
    model = Dense(32, activation='relu')
    
    
    #model.add(MaxPool2D((2,2))) # Stride 2?
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(10, activation='softmax'))
    model.summary()
    model.compile(loss = 'categorical_crossentropy',
                optimizer = 'adam',
                metrics = ['acc'])
    return model

Plan of attack:¶

We will run CNN and RNN models with increasing data size at each stage to mitigate computing resources.
If considerable accuracy is not reached, we will then begin tweaking the models, both original and pre-trained.

1a. Building CNN Model¶

# config_t = tf.ConfigProto()
# config_t.gpu_options.allow_growth = True
# session = tf.Session(config=config_t)
# set_session(session)

config.mode ='conv'
print('Starting Conv Model')
X,y = build_rand_feat() # Build feature set from random choices
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2],1)
print('Getting Conv Model')
model = get_conv_model()

Starting Conv Model
Loading existing data for conv model
Dataframe shape (2000, 2)
<__main__.Config object at 0x0000012E518F9160>
Getting Conv Model
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_22 (Conv2D)           (None, 9, 13, 16)         160       
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 9, 13, 32)         4640      
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 9, 13, 64)         18496     
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 9, 13, 128)        73856     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 4, 6, 128)         0         
_________________________________________________________________
dropout_7 (Dropout)          (None, 4, 6, 128)         0         
_________________________________________________________________
flatten_5 (Flatten)          (None, 3072)              0         
_________________________________________________________________
dense_13 (Dense)             (None, 128)               393344    
_________________________________________________________________
dense_14 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_15 (Dense)             (None, 10)                650       
=================================================================
Total params: 499,402
Trainable params: 499,402
Non-trainable params: 0
_________________________________________________________________

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'accuracy', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period = 1 )

model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         )

Train on 214920 samples, validate on 23881 samples
Epoch 1/10
214920/214920 [==============================] - 513s 2ms/step - loss: 1.0823 - acc: 0.6590 - val_loss: 0.7372 - val_acc: 0.7753
Epoch 2/10
    64/214920 [..............................] - ETA: 10:52 - loss: 0.9853 - acc: 0.7188

C:\Users\abhin\Anaconda3\lib\site-packages\keras\callbacks.py:434: RuntimeWarning: Can save best model only with accuracy available, skipping.
  'skipping.' % (self.monitor), RuntimeWarning)

214920/214920 [==============================] - 487s 2ms/step - loss: 0.7295 - acc: 0.7743 - val_loss: 0.5727 - val_acc: 0.8255
Epoch 3/10
214920/214920 [==============================] - 484s 2ms/step - loss: 0.6341 - acc: 0.8037 - val_loss: 0.5024 - val_acc: 0.8521
Epoch 4/10
214920/214920 [==============================] - 481s 2ms/step - loss: 0.5828 - acc: 0.8200 - val_loss: 0.4650 - val_acc: 0.8627
Epoch 5/10
214920/214920 [==============================] - 478s 2ms/step - loss: 0.5489 - acc: 0.8310 - val_loss: 0.4699 - val_acc: 0.8585
Epoch 6/10
214920/214920 [==============================] - 501s 2ms/step - loss: 0.5251 - acc: 0.8396 - val_loss: 0.4533 - val_acc: 0.8650
Epoch 7/10
214920/214920 [==============================] - 480s 2ms/step - loss: 0.5068 - acc: 0.8443 - val_loss: 0.4006 - val_acc: 0.8819
Epoch 8/10
214920/214920 [==============================] - 488s 2ms/step - loss: 0.4905 - acc: 0.8496 - val_loss: 0.3684 - val_acc: 0.8951
Epoch 9/10
214920/214920 [==============================] - 506s 2ms/step - loss: 0.4816 - acc: 0.8533 - val_loss: 0.3993 - val_acc: 0.8813
Epoch 10/10
214920/214920 [==============================] - 500s 2ms/step - loss: 0.4742 - acc: 0.8563 - val_loss: 0.3768 - val_acc: 0.8893

<keras.callbacks.History at 0x12e518dc390>

#CNN Accuracy # val_acc improved from 0.92240 to 0.92523
model.save(config.model_path)

df2.shape, df_select.shape
#df2.head()

((2000, 2), (3000, 3))

Preparing test data for prediction¶

# Filtering untrained filenames from whole dataset of df_select
rem_files = pd.DataFrame(set(df_select.fname.values) - set(df2.index.values))

# Building the dataframe for untouched files
rd = pd.DataFrame()
for f in rem_files[0]:
    r = df_select[df_select.fname==f]
    rd = rd.append(r)

rd.head(), rd.shape

(             fname      label  manually_verified
 5121  8ae68e01.wav      Flute                  0
 8896  f040db67.wav      Flute                  1
 8932  f1119913.wav  Bass_drum                  0
 1897  34a90030.wav     Hi-hat                  1
 1290  238b16a5.wav      Cello                  1, (1000, 3))

# Building a test dataset of 1000 wav files
df_t = rd.sample(frac=1, random_state=123)

df_t.shape

(1000, 3)

df_t.head()

# Transferring test files from audio_train to test_data folder
for f in df_t.fname:
    shutil.copy('./freesound-audio-tagging/clean/'+f, './freesound-audio-tagging/test_files/')

len(os.listdir('./freesound-audio-tagging/test_files/'))

1000

Prediction using CNN¶

def build_predictions(audio_dir):
    y_true = []
    y_pred = []
    fn_prob = {}
    
    print('Extracting features from audio')
    for fn in tqdm_notebook(os.listdir(audio_dir)):
        rate, wav = wavfile.read(os.path.join(audio_dir, fn))
        label = fn2class[fn]
        c = classes.index(label)
        y_prob = []
        
        for i in range(0, wav.shape[0] - config.step, config.step):
            sample = wav[i:i+config.step]
            x = mfcc(sample, rate, numcep=config.nfeat, nfilt = config.nfilt,
                    nfft = config.nfft)
            x = (x - config.min)/(config.max - config.min)
            
            if config.mode == 'conv':
                x = x.reshape(1, x.shape[0], x.shape[1],1)
            elif config.mode =='time':
                x = np.expand_dims(x, axis = 0)
            
            y_hat = model.predict(x)
            y_prob.append(y_hat)
            y_pred.append(np.argmax(y_hat))
            y_true.append(c)
            
        fn_prob[fn] = np.mean(y_prob, axis = 0).flatten()
    return y_true, y_pred, fn_prob

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'conv.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true, y_pred, fn_prob = build_predictions('./freesound-audio-tagging/test_files')
acc_score = accuracy_score(y_true = y_true, y_pred = y_pred)

y_probs = []

for i, row in df_t.iterrows():
    y_prob = fn_prob[row.fname]
    y_probs.append(y_prob)
    for c, p in zip(classes, y_prob):
        df_t.at[i,c] = p

y_pred = [classes[np.argmax(y)] for y in y_probs]
df_t['y_pred'] = y_pred

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
  import sys

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

df_t.to_csv('./freesound-audio-tagging/predictions.csv', index = False)

acc_score

0.6159695817490495

1b. Building RNN Model¶

config = Config(mode='time')
print('Building RNN Model')
X,y = build_rand_feat()
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2])
print('Retrieving RNN Model')
model = get_recurrent_model()

Building RNN Model
Dataframe shape (2000, 3)

No. of small files that did not make it to train set: 66
Retrieving RNN Model
Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_1 (LSTM)                (None, 9, 128)            72704     
_________________________________________________________________
lstm_2 (LSTM)                (None, 9, 128)            131584    
_________________________________________________________________
dropout_4 (Dropout)          (None, 9, 128)            0         
_________________________________________________________________
time_distributed_1 (TimeDist (None, 9, 64)             8256      
_________________________________________________________________
time_distributed_2 (TimeDist (None, 9, 32)             2080      
_________________________________________________________________
time_distributed_3 (TimeDist (None, 9, 16)             528       
_________________________________________________________________
time_distributed_4 (TimeDist (None, 9, 8)              136       
_________________________________________________________________
flatten_4 (Flatten)          (None, 72)                0         
_________________________________________________________________
dense_14 (Dense)             (None, 10)                730       
=================================================================
Total params: 216,018
Trainable params: 216,018
Non-trainable params: 0
_________________________________________________________________

X.shape

(238806, 9, 13)

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)

model.fit(X,y, epochs = 25, batch_size = 64, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 111450 samples, validate on 12384 samples
Epoch 1/10
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acc: 0.432 - ETA: 0s - loss: 1.5387 - acc: 0.433 - ETA: 0s - loss: 1.5386 - acc: 0.433 - ETA: 0s - loss: 1.5384 - acc: 0.433 - ETA: 0s - loss: 1.5381 - acc: 0.433 - ETA: 0s - loss: 1.5376 - acc: 0.433 - ETA: 0s - loss: 1.5372 - acc: 0.433 - 54s 480us/step - loss: 1.5371 - acc: 0.4337 - val_loss: 1.2795 - val_acc: 0.5472

Epoch 00001: val_acc improved from -inf to 0.54724, saving model to ./freesound-audio-tagging/models\time.model
Epoch 2/10
 26208/111450 [======>.......................] - ETA: 58s - loss: 1.6125 - acc: 0.50 - ETA: 47s - loss: 1.4223 - acc: 0.50 - ETA: 46s - loss: 1.3508 - acc: 0.51 - ETA: 46s - loss: 1.3190 - acc: 0.52 - ETA: 46s - loss: 1.3210 - acc: 0.51 - ETA: 46s - loss: 1.2883 - acc: 0.52 - ETA: 45s - loss: 1.3053 - acc: 0.52 - ETA: 46s - loss: 1.2892 - acc: 0.53 - ETA: 46s - loss: 1.3102 - acc: 0.52 - ETA: 46s - loss: 1.2997 - acc: 0.52 - ETA: 46s - loss: 1.3046 - acc: 0.52 - ETA: 46s - loss: 1.2994 - acc: 0.52 - ETA: 46s - loss: 1.2982 - acc: 0.52 - ETA: 46s - loss: 1.2935 - acc: 0.52 - ETA: 46s - loss: 1.2873 - acc: 0.53 - ETA: 46s - loss: 1.2853 - acc: 0.52 - ETA: 46s - loss: 1.2864 - acc: 0.52 - ETA: 46s - loss: 1.2971 - acc: 0.52 - ETA: 47s - loss: 1.3027 - acc: 0.51 - ETA: 47s - loss: 1.3058 - acc: 0.51 - ETA: 47s - loss: 1.3001 - acc: 0.51 - ETA: 47s - loss: 1.2941 - acc: 0.52 - ETA: 47s - loss: 1.2917 - acc: 0.52 - ETA: 46s - loss: 1.2992 - acc: 0.52 - ETA: 46s - loss: 1.2978 - acc: 0.52 - ETA: 46s - 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Epoch 00002: val_acc improved from 0.54724 to 0.64535, saving model to ./freesound-audio-tagging/models\time.model
Epoch 3/10
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acc: 0.660 - ETA: 0s - loss: 0.9794 - acc: 0.660 - 57s 511us/step - loss: 0.9793 - acc: 0.6603 - val_loss: 0.8698 - val_acc: 0.7035

Epoch 00003: val_acc improved from 0.64535 to 0.70349, saving model to ./freesound-audio-tagging/models\time.model
Epoch 4/10
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acc: 0.707 - ETA: 0s - loss: 0.8527 - acc: 0.707 - ETA: 0s - loss: 0.8526 - acc: 0.707 - ETA: 0s - loss: 0.8525 - acc: 0.707 - ETA: 0s - loss: 0.8524 - acc: 0.707 - 53s 475us/step - loss: 0.8523 - acc: 0.7072 - val_loss: 0.7369 - val_acc: 0.7492

Epoch 00004: val_acc improved from 0.70349 to 0.74919, saving model to ./freesound-audio-tagging/models\time.model
Epoch 5/10
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Epoch 00005: val_acc improved from 0.74919 to 0.78391, saving model to ./freesound-audio-tagging/models\time.model
Epoch 6/10
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Epoch 00006: val_acc improved from 0.78391 to 0.79724, saving model to ./freesound-audio-tagging/models\time.model
Epoch 7/10
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Epoch 00007: val_acc improved from 0.79724 to 0.79974, saving model to ./freesound-audio-tagging/models\time.model
Epoch 8/10
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Epoch 00008: val_acc improved from 0.79974 to 0.82687, saving model to ./freesound-audio-tagging/models\time.model
Epoch 9/10
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Epoch 00009: val_acc did not improve from 0.82687
Epoch 10/10
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acc: 0.827 - ETA: 0s - loss: 0.5156 - acc: 0.827 - ETA: 0s - loss: 0.5155 - acc: 0.827 - ETA: 0s - loss: 0.5155 - acc: 0.827 - ETA: 0s - loss: 0.5156 - acc: 0.827 - ETA: 0s - loss: 0.5160 - acc: 0.827 - ETA: 0s - loss: 0.5161 - acc: 0.827 - ETA: 0s - loss: 0.5162 - acc: 0.827 - ETA: 0s - loss: 0.5162 - acc: 0.827 - ETA: 0s - loss: 0.5164 - acc: 0.827 - ETA: 0s - loss: 0.5165 - acc: 0.827 - ETA: 0s - loss: 0.5165 - acc: 0.827 - ETA: 0s - loss: 0.5166 - acc: 0.827 - ETA: 0s - loss: 0.5166 - acc: 0.827 - ETA: 0s - loss: 0.5164 - acc: 0.827 - 54s 480us/step - loss: 0.5164 - acc: 0.8273 - val_loss: 0.4719 - val_acc: 0.8479

Epoch 00010: val_acc improved from 0.82687 to 0.84795, saving model to ./freesound-audio-tagging/models\time.model

# val_acc improved from 0.82687 to 0.84795

Prediction using RNN Model¶

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'time.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true_r, y_pred_r, fn_prob_r = build_predictions('./freesound-audio-tagging/test_files')
acc_score_r = accuracy_score(y_true = y_true_r, y_pred = y_pred_r)

y_probs_r = []

for i, row in df_t.iterrows():
    y_prob_r = fn_prob_r[row.fname]
    y_probs_r.append(y_prob_r)
    for c, p in zip(classes, y_prob_r):
        df_t.at[i,c] = p

y_pred_r = [classes[np.argmax(y)] for y in y_probs_r]
df_t['y_pred_r'] = y_pred_r

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

df_t.to_csv('./freesound-audio-tagging/predictions_rnn.csv', index = False)

acc_score_r

0.5487816458701309

df_t.head()

df_select.shape

(3000, 3)

Using training data that is manually verified as 1¶

df_select[df_select.manually_verified ==1 ].groupby('label').count()

df2 = df_select[df_select.manually_verified ==1 ]
df2.set_index('fname', inplace = True)

df2.shape

(1312, 2)

df2.head()

2a. Building CNN with Manually Verified data¶

config = Config('conv')

print('Building X, y train data for CNN Model')
X,y = build_rand_feat() # Build feature set from random choices
print('X, y built for train')
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2],1)
print('Building CNN Model')
model = get_conv_model()

Starting Conv Model

Getting Conv Model
Model: "sequential_16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_21 (Conv2D)           (None, 9, 13, 16)         160       
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 9, 13, 32)         4640      
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 9, 13, 64)         18496     
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 9, 13, 128)        73856     
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 4, 6, 128)         0         
_________________________________________________________________
dropout_15 (Dropout)         (None, 4, 6, 128)         0         
_________________________________________________________________
flatten_15 (Flatten)         (None, 3072)              0         
_________________________________________________________________
dense_61 (Dense)             (None, 128)               393344    
_________________________________________________________________
dense_62 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_63 (Dense)             (None, 10)                650       
=================================================================
Total params: 499,402
Trainable params: 499,402
Non-trainable params: 0
_________________________________________________________________

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)

model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 111450 samples, validate on 12384 samples
Epoch 1/10
 11616/111450 [==>...........................] - ETA: 31:08 - loss: 2.2899 - acc: 0.15 - ETA: 12:08 - loss: 2.2745 - acc: 0.16 - ETA: 8:17 - loss: 2.2204 - acc: 0.1500 - ETA: 6:39 - loss: 2.2401 - acc: 0.151 - ETA: 5:43 - loss: 2.2367 - acc: 0.156 - ETA: 5:09 - loss: 2.2317 - acc: 0.170 - ETA: 4:44 - loss: 2.2305 - acc: 0.170 - ETA: 4:26 - loss: 2.2376 - acc: 0.164 - ETA: 4:12 - loss: 2.2434 - acc: 0.165 - ETA: 4:01 - loss: 2.2410 - acc: 0.169 - ETA: 3:52 - loss: 2.2372 - acc: 0.168 - ETA: 3:45 - loss: 2.2310 - acc: 0.172 - ETA: 3:39 - loss: 2.2284 - acc: 0.177 - ETA: 3:34 - loss: 2.2260 - acc: 0.180 - ETA: 3:29 - loss: 2.2185 - acc: 0.187 - ETA: 3:26 - loss: 2.2218 - acc: 0.185 - ETA: 3:25 - loss: 2.2178 - acc: 0.187 - ETA: 3:21 - loss: 2.2086 - acc: 0.195 - ETA: 3:19 - loss: 2.2062 - acc: 0.198 - ETA: 3:16 - loss: 2.2023 - acc: 0.200 - ETA: 3:14 - loss: 2.2029 - acc: 0.198 - ETA: 3:11 - loss: 2.2015 - acc: 0.196 - ETA: 3:09 - loss: 2.1963 - acc: 0.200 - ETA: 3:07 - loss: 2.1903 - acc: 0.203 - 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acc: 0.692 - ETA: 1s - loss: 0.8689 - acc: 0.692 - ETA: 1s - loss: 0.8689 - acc: 0.692 - ETA: 1s - loss: 0.8688 - acc: 0.692 - ETA: 1s - loss: 0.8686 - acc: 0.692 - ETA: 1s - loss: 0.8686 - acc: 0.692 - ETA: 1s - loss: 0.8684 - acc: 0.692 - ETA: 1s - loss: 0.8681 - acc: 0.692 - ETA: 1s - loss: 0.8680 - acc: 0.692 - ETA: 0s - loss: 0.8680 - acc: 0.692 - ETA: 0s - loss: 0.8678 - acc: 0.692 - ETA: 0s - loss: 0.8676 - acc: 0.692 - ETA: 0s - loss: 0.8676 - acc: 0.692 - ETA: 0s - loss: 0.8675 - acc: 0.692 - ETA: 0s - loss: 0.8673 - acc: 0.692 - ETA: 0s - loss: 0.8671 - acc: 0.692 - ETA: 0s - loss: 0.8669 - acc: 0.692 - ETA: 0s - loss: 0.8670 - acc: 0.692 - ETA: 0s - loss: 0.8669 - acc: 0.692 - ETA: 0s - loss: 0.8667 - acc: 0.692 - 171s 2ms/step - loss: 0.8667 - acc: 0.6929 - val_loss: 0.5091 - val_acc: 0.8247

Epoch 00001: val_acc improved from -inf to 0.82469, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 2/10
 10560/111450 [=>............................] - ETA: 2:46 - loss: 0.3232 - acc: 0.906 - ETA: 2:38 - loss: 0.5057 - acc: 0.812 - ETA: 2:36 - loss: 0.5252 - acc: 0.831 - ETA: 2:36 - loss: 0.5857 - acc: 0.799 - ETA: 2:36 - loss: 0.5730 - acc: 0.805 - ETA: 2:36 - loss: 0.5335 - acc: 0.826 - ETA: 2:37 - loss: 0.5333 - acc: 0.826 - ETA: 2:37 - loss: 0.5487 - acc: 0.831 - ETA: 2:36 - loss: 0.5466 - acc: 0.827 - ETA: 2:38 - loss: 0.5336 - acc: 0.832 - ETA: 2:40 - loss: 0.5270 - acc: 0.835 - ETA: 2:41 - loss: 0.5273 - acc: 0.833 - ETA: 2:42 - loss: 0.5254 - acc: 0.832 - ETA: 2:42 - loss: 0.5329 - acc: 0.828 - ETA: 2:44 - loss: 0.5462 - acc: 0.821 - ETA: 2:45 - loss: 0.5517 - acc: 0.816 - ETA: 2:45 - loss: 0.5496 - acc: 0.813 - ETA: 2:45 - loss: 0.5493 - acc: 0.814 - ETA: 2:44 - loss: 0.5458 - acc: 0.818 - ETA: 2:44 - loss: 0.5437 - acc: 0.821 - ETA: 2:44 - loss: 0.5454 - acc: 0.820 - ETA: 2:44 - loss: 0.5459 - acc: 0.820 - ETA: 2:44 - loss: 0.5390 - acc: 0.821 - ETA: 2:43 - loss: 0.5276 - acc: 0.823 - 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Epoch 00002: val_acc improved from 0.82469 to 0.88356, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 3/10
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ETA: 0s - loss: 0.3789 - acc: 0.867 - 176s 2ms/step - loss: 0.3789 - acc: 0.8672 - val_loss: 0.3306 - val_acc: 0.8877

Epoch 00003: val_acc improved from 0.88356 to 0.88768, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 4/10
  9248/111450 [=>............................] - ETA: 2:42 - loss: 0.5137 - acc: 0.843 - ETA: 2:35 - loss: 0.4858 - acc: 0.854 - ETA: 2:40 - loss: 0.4304 - acc: 0.875 - ETA: 2:39 - loss: 0.3779 - acc: 0.892 - ETA: 2:39 - loss: 0.4113 - acc: 0.878 - ETA: 2:39 - loss: 0.4055 - acc: 0.872 - ETA: 2:40 - loss: 0.3991 - acc: 0.872 - ETA: 2:39 - loss: 0.4046 - acc: 0.872 - ETA: 2:39 - loss: 0.4179 - acc: 0.864 - ETA: 2:40 - loss: 0.4155 - acc: 0.859 - ETA: 2:41 - loss: 0.4138 - acc: 0.860 - ETA: 2:42 - loss: 0.4067 - acc: 0.861 - ETA: 2:42 - loss: 0.4067 - acc: 0.862 - ETA: 2:42 - loss: 0.4034 - acc: 0.865 - ETA: 2:43 - loss: 0.3964 - acc: 0.866 - ETA: 2:43 - loss: 0.3942 - acc: 0.866 - ETA: 2:44 - loss: 0.3974 - acc: 0.862 - ETA: 2:44 - loss: 0.3938 - acc: 0.860 - ETA: 2:44 - loss: 0.3843 - acc: 0.863 - ETA: 2:43 - loss: 0.3762 - acc: 0.867 - ETA: 2:43 - loss: 0.3738 - acc: 0.869 - ETA: 2:43 - loss: 0.3714 - acc: 0.870 - ETA: 2:45 - loss: 0.3737 - acc: 0.871 - ETA: 2:46 - loss: 0.3736 - acc: 0.870 - 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acc: 0.885 - ETA: 0s - loss: 0.3227 - acc: 0.885 - ETA: 0s - loss: 0.3228 - acc: 0.885 - ETA: 0s - loss: 0.3227 - acc: 0.885 - ETA: 0s - loss: 0.3226 - acc: 0.885 - ETA: 0s - loss: 0.3226 - acc: 0.885 - 169s 2ms/step - loss: 0.3226 - acc: 0.8858 - val_loss: 0.2470 - val_acc: 0.9159

Epoch 00004: val_acc improved from 0.88768 to 0.91586, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 5/10
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acc: 0.900 - ETA: 1s - loss: 0.2834 - acc: 0.900 - ETA: 1s - loss: 0.2835 - acc: 0.900 - ETA: 1s - loss: 0.2835 - acc: 0.900 - ETA: 1s - loss: 0.2834 - acc: 0.900 - ETA: 1s - loss: 0.2834 - acc: 0.900 - ETA: 1s - loss: 0.2834 - acc: 0.900 - ETA: 1s - loss: 0.2834 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2836 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - ETA: 0s - loss: 0.2835 - acc: 0.900 - 175s 2ms/step - loss: 0.2834 - acc: 0.9002 - val_loss: 0.2170 - val_acc: 0.9241

Epoch 00005: val_acc improved from 0.91586 to 0.92410, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 6/10
 10912/111450 [=>............................] - ETA: 2:53 - loss: 0.3003 - acc: 0.906 - ETA: 2:43 - loss: 0.1736 - acc: 0.947 - ETA: 2:46 - loss: 0.1466 - acc: 0.960 - ETA: 2:44 - loss: 0.2076 - acc: 0.932 - ETA: 2:46 - loss: 0.1946 - acc: 0.937 - ETA: 2:48 - loss: 0.1862 - acc: 0.937 - ETA: 2:51 - loss: 0.1913 - acc: 0.934 - ETA: 2:52 - loss: 0.2037 - acc: 0.934 - ETA: 2:53 - loss: 0.2011 - acc: 0.934 - ETA: 2:54 - loss: 0.2191 - acc: 0.929 - ETA: 2:55 - loss: 0.2191 - acc: 0.925 - ETA: 2:55 - loss: 0.2187 - acc: 0.928 - ETA: 2:55 - loss: 0.2213 - acc: 0.925 - ETA: 2:53 - loss: 0.2362 - acc: 0.921 - ETA: 2:52 - loss: 0.2295 - acc: 0.924 - ETA: 2:51 - loss: 0.2348 - acc: 0.922 - ETA: 2:49 - loss: 0.2477 - acc: 0.921 - ETA: 2:48 - loss: 0.2776 - acc: 0.911 - ETA: 2:47 - loss: 0.2745 - acc: 0.910 - ETA: 2:47 - loss: 0.2844 - acc: 0.908 - ETA: 2:46 - loss: 0.2889 - acc: 0.909 - ETA: 2:45 - loss: 0.2926 - acc: 0.908 - ETA: 2:45 - loss: 0.2882 - acc: 0.909 - ETA: 2:45 - loss: 0.2864 - acc: 0.909 - 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Epoch 00006: val_acc improved from 0.92410 to 0.92426, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 7/10
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acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2425 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 1s - loss: 0.2424 - acc: 0.915 - ETA: 0s - loss: 0.2424 - acc: 0.915 - ETA: 0s - loss: 0.2423 - acc: 0.915 - ETA: 0s - loss: 0.2423 - acc: 0.915 - ETA: 0s - loss: 0.2423 - acc: 0.915 - ETA: 0s - loss: 0.2423 - acc: 0.915 - ETA: 0s - loss: 0.2422 - acc: 0.915 - ETA: 0s - loss: 0.2422 - acc: 0.915 - ETA: 0s - loss: 0.2422 - acc: 0.915 - ETA: 0s - loss: 0.2422 - acc: 0.915 - ETA: 0s - loss: 0.2421 - acc: 0.915 - ETA: 0s - loss: 0.2421 - acc: 0.915 - ETA: 0s - loss: 0.2421 - acc: 0.915 - ETA: 0s - loss: 0.2421 - acc: 0.915 - 174s 2ms/step - loss: 0.2421 - acc: 0.9155 - val_loss: 0.1886 - val_acc: 0.9333

Epoch 00007: val_acc improved from 0.92426 to 0.93330, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 8/10
  8032/111450 [=>............................] - ETA: 3:00 - loss: 0.2227 - acc: 0.906 - ETA: 2:55 - loss: 0.2233 - acc: 0.906 - ETA: 2:56 - loss: 0.1885 - acc: 0.921 - ETA: 2:57 - loss: 0.1788 - acc: 0.925 - ETA: 2:57 - loss: 0.1598 - acc: 0.937 - ETA: 3:01 - loss: 0.1385 - acc: 0.946 - ETA: 3:03 - loss: 0.1529 - acc: 0.937 - ETA: 3:05 - loss: 0.1686 - acc: 0.937 - ETA: 3:05 - loss: 0.1768 - acc: 0.931 - ETA: 3:06 - loss: 0.1726 - acc: 0.931 - ETA: 3:07 - loss: 0.1852 - acc: 0.929 - ETA: 3:07 - loss: 0.1830 - acc: 0.925 - ETA: 3:07 - loss: 0.1821 - acc: 0.928 - ETA: 3:04 - loss: 0.2304 - acc: 0.914 - ETA: 3:03 - loss: 0.2211 - acc: 0.919 - ETA: 3:02 - loss: 0.2286 - acc: 0.914 - ETA: 3:00 - loss: 0.2320 - acc: 0.913 - ETA: 2:59 - loss: 0.2207 - acc: 0.919 - ETA: 2:59 - loss: 0.2163 - acc: 0.920 - ETA: 2:59 - loss: 0.2125 - acc: 0.921 - ETA: 2:57 - loss: 0.2121 - acc: 0.922 - ETA: 2:56 - loss: 0.2163 - acc: 0.922 - ETA: 2:55 - loss: 0.2139 - acc: 0.923 - ETA: 2:55 - loss: 0.2128 - acc: 0.923 - 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Epoch 00008: val_acc improved from 0.93330 to 0.93475, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 9/10
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Epoch 00009: val_acc improved from 0.93475 to 0.94138, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 10/10
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ETA: 0s - loss: 0.2047 - acc: 0.927 - ETA: 0s - loss: 0.2047 - acc: 0.927 - ETA: 0s - loss: 0.2047 - acc: 0.927 - ETA: 0s - loss: 0.2047 - acc: 0.927 - ETA: 0s - loss: 0.2047 - acc: 0.927 - ETA: 0s - loss: 0.2048 - acc: 0.927 - 186s 2ms/step - loss: 0.2048 - acc: 0.9272 - val_loss: 0.1622 - val_acc: 0.9448

Epoch 00010: val_acc improved from 0.94138 to 0.94477, saving model to ./freesound-audio-tagging/models\conv.model

# Epoch 00010: val_acc improved from 0.94138 to 0.94477

Preparing Test Data¶

rem_files = pd.DataFrame(set(df_select.fname.values) - set(df2.index.values))

rd = pd.DataFrame()
for f in rem_files[0]:
    r = df_select[df_select.fname==f]
    rd = rd.append(r)

rd.shape

(1688, 3)

rd.head()

df_t = rd

df_t.head(1), df_t.shape

(             fname  label  manually_verified
 1353  253b91fe.wav  Flute                  0, (1688, 3))

for f in tqdm_notebook(df_t.fname):
    shutil.copy('./freesound-audio-tagging/clean/'+f, './freesound-audio-tagging/test_files/')
print('Number of test files: ',len(os.listdir('./freesound-audio-tagging/test_files/')))

Number of test files:  1688

Predicting with CNN¶

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'conv.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true, y_pred, fn_prob = build_predictions('./freesound-audio-tagging/test_files')
acc_score = accuracy_score(y_true = y_true, y_pred = y_pred)

y_probs = []

for i, row in df_t.iterrows():
    y_prob = fn_prob[row.fname]
    y_probs.append(y_prob)
    for c, p in zip(classes, y_prob):
        df_t.at[i,c] = p

y_pred = [classes[np.argmax(y)] for y in y_probs]
df_t['y_pred'] = y_pred

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

df_t.to_csv('./freesound-audio-tagging/predictions_cnn2.csv', index = False)

print('Test Accuracy Score', acc_score)

Test Accuracy Score 0.44766480353367244

2b. Build RNN Model¶

config = Config(mode='time')
print('Building RNN Model')
X,y = build_rand_feat()
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2])
print('Retrieving RNN Model')
model = get_recurrent_model()

Building RNN Model
Dataframe shape (1312, 2)

Retrieving RNN Model
Model: "sequential_18"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_23 (LSTM)               (None, 9, 128)            72704     
_________________________________________________________________
lstm_24 (LSTM)               (None, 9, 128)            131584    
_________________________________________________________________
dropout_17 (Dropout)         (None, 9, 128)            0         
_________________________________________________________________
time_distributed_41 (TimeDis (None, 9, 64)             8256      
_________________________________________________________________
time_distributed_42 (TimeDis (None, 9, 32)             2080      
_________________________________________________________________
time_distributed_43 (TimeDis (None, 9, 16)             528       
_________________________________________________________________
time_distributed_44 (TimeDis (None, 9, 8)              136       
_________________________________________________________________
flatten_17 (Flatten)         (None, 72)                0         
_________________________________________________________________
dense_73 (Dense)             (None, 10)                730       
=================================================================
Total params: 216,018
Trainable params: 216,018
Non-trainable params: 0
_________________________________________________________________

Fit RNN Model¶

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)

model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 111450 samples, validate on 12384 samples
Epoch 1/10
 12992/111450 [==>...........................] - ETA: 47:50 - loss: 2.2997 - acc: 0.15 - ETA: 25:47 - loss: 2.3035 - acc: 0.09 - ETA: 13:40 - loss: 2.3021 - acc: 0.12 - ETA: 9:38 - loss: 2.3019 - acc: 0.0990 - ETA: 7:37 - loss: 2.2960 - acc: 0.140 - ETA: 6:24 - loss: 2.2934 - acc: 0.140 - ETA: 5:35 - loss: 2.2932 - acc: 0.138 - ETA: 5:00 - loss: 2.2852 - acc: 0.151 - ETA: 4:34 - loss: 2.2811 - acc: 0.146 - ETA: 4:14 - loss: 2.2724 - acc: 0.156 - ETA: 3:58 - loss: 2.2752 - acc: 0.150 - ETA: 3:45 - loss: 2.2803 - acc: 0.142 - ETA: 3:34 - loss: 2.2813 - acc: 0.138 - ETA: 3:25 - loss: 2.2787 - acc: 0.138 - ETA: 3:16 - loss: 2.2794 - acc: 0.135 - ETA: 3:10 - loss: 2.2791 - acc: 0.133 - ETA: 3:03 - loss: 2.2779 - acc: 0.136 - ETA: 3:02 - loss: 2.2779 - acc: 0.136 - ETA: 2:57 - loss: 2.2751 - acc: 0.140 - ETA: 2:52 - loss: 2.2724 - acc: 0.142 - ETA: 2:48 - loss: 2.2695 - acc: 0.145 - ETA: 2:44 - loss: 2.2642 - acc: 0.149 - ETA: 2:41 - loss: 2.2639 - acc: 0.148 - ETA: 2:38 - loss: 2.2642 - acc: 0.151 - 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acc: 0.490 - ETA: 2s - loss: 1.4012 - acc: 0.490 - ETA: 2s - loss: 1.4010 - acc: 0.490 - ETA: 2s - loss: 1.4007 - acc: 0.490 - ETA: 2s - loss: 1.4005 - acc: 0.490 - ETA: 2s - loss: 1.4002 - acc: 0.490 - ETA: 2s - loss: 1.4000 - acc: 0.490 - ETA: 2s - loss: 1.3997 - acc: 0.490 - ETA: 2s - loss: 1.3994 - acc: 0.490 - ETA: 2s - loss: 1.3993 - acc: 0.4908111450/111450 [==============================] - ETA: 2s - loss: 1.3993 - acc: 0.490 - ETA: 2s - loss: 1.3994 - acc: 0.490 - ETA: 2s - loss: 1.3993 - acc: 0.490 - ETA: 1s - loss: 1.3991 - acc: 0.490 - ETA: 1s - loss: 1.3992 - acc: 0.490 - ETA: 1s - loss: 1.3988 - acc: 0.491 - ETA: 1s - loss: 1.3986 - acc: 0.491 - ETA: 1s - loss: 1.3984 - acc: 0.491 - ETA: 1s - loss: 1.3982 - acc: 0.491 - ETA: 1s - loss: 1.3981 - acc: 0.491 - ETA: 1s - loss: 1.3979 - acc: 0.491 - ETA: 1s - loss: 1.3977 - acc: 0.491 - ETA: 1s - loss: 1.3974 - acc: 0.491 - ETA: 1s - loss: 1.3972 - acc: 0.491 - ETA: 1s - loss: 1.3971 - acc: 0.491 - ETA: 1s - loss: 1.3970 - acc: 0.491 - 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Epoch 00001: val_acc improved from -inf to 0.65480, saving model to ./freesound-audio-tagging/models\time.model
Epoch 2/10
 13216/111450 [==>...........................] - ETA: 2:04 - loss: 1.2717 - acc: 0.593 - ETA: 1:45 - loss: 1.0247 - acc: 0.656 - ETA: 1:38 - loss: 1.0115 - acc: 0.668 - ETA: 1:33 - loss: 0.9766 - acc: 0.664 - ETA: 1:32 - loss: 0.9753 - acc: 0.665 - ETA: 1:31 - loss: 1.0134 - acc: 0.656 - ETA: 1:29 - loss: 1.0688 - acc: 0.641 - ETA: 1:30 - loss: 1.0681 - acc: 0.637 - ETA: 1:30 - loss: 1.0731 - acc: 0.636 - ETA: 1:30 - loss: 1.0589 - acc: 0.636 - ETA: 1:29 - loss: 1.0812 - acc: 0.621 - ETA: 1:28 - loss: 1.0869 - acc: 0.620 - ETA: 1:28 - loss: 1.0933 - acc: 0.615 - ETA: 1:28 - loss: 1.1028 - acc: 0.606 - ETA: 1:28 - loss: 1.0880 - acc: 0.611 - ETA: 1:28 - loss: 1.0853 - acc: 0.613 - ETA: 1:28 - loss: 1.0950 - acc: 0.613 - ETA: 1:28 - loss: 1.0898 - acc: 0.617 - ETA: 1:28 - loss: 1.0798 - acc: 0.619 - ETA: 1:28 - loss: 1.0651 - acc: 0.624 - ETA: 1:28 - loss: 1.0725 - acc: 0.623 - ETA: 1:28 - loss: 1.0696 - acc: 0.621 - ETA: 1:28 - loss: 1.0693 - acc: 0.623 - ETA: 1:28 - loss: 1.0740 - acc: 0.621 - 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acc: 0.680 - ETA: 0s - loss: 0.9097 - acc: 0.680 - ETA: 0s - loss: 0.9097 - acc: 0.680 - ETA: 0s - loss: 0.9097 - acc: 0.680 - ETA: 0s - loss: 0.9098 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9096 - acc: 0.680 - ETA: 0s - loss: 0.9097 - acc: 0.680 - 103s 925us/step - loss: 0.9096 - acc: 0.6809 - val_loss: 0.7704 - val_acc: 0.7314

Epoch 00002: val_acc improved from 0.65480 to 0.73143, saving model to ./freesound-audio-tagging/models\time.model
Epoch 3/10
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ETA: 0s - loss: 0.7398 - acc: 0.740 - ETA: 0s - loss: 0.7398 - acc: 0.740 - ETA: 0s - loss: 0.7397 - acc: 0.741 - ETA: 0s - loss: 0.7396 - acc: 0.741 - ETA: 0s - loss: 0.7397 - acc: 0.740 - 94s 845us/step - loss: 0.7396 - acc: 0.7410 - val_loss: 0.6347 - val_acc: 0.7802

Epoch 00003: val_acc improved from 0.73143 to 0.78020, saving model to ./freesound-audio-tagging/models\time.model
Epoch 4/10
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Epoch 00004: val_acc improved from 0.78020 to 0.81444, saving model to ./freesound-audio-tagging/models\time.model
Epoch 5/10
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Epoch 00005: val_acc improved from 0.81444 to 0.83939, saving model to ./freesound-audio-tagging/models\time.model
Epoch 6/10
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acc: 0.826 - ETA: 0s - loss: 0.5018 - acc: 0.826 - ETA: 0s - loss: 0.5019 - acc: 0.826 - ETA: 0s - loss: 0.5018 - acc: 0.826 - ETA: 0s - loss: 0.5019 - acc: 0.826 - ETA: 0s - loss: 0.5018 - acc: 0.826 - ETA: 0s - loss: 0.5017 - acc: 0.826 - ETA: 0s - loss: 0.5017 - acc: 0.826 - ETA: 0s - loss: 0.5017 - acc: 0.826 - ETA: 0s - loss: 0.5017 - acc: 0.826 - ETA: 0s - loss: 0.5017 - acc: 0.826 - ETA: 0s - loss: 0.5016 - acc: 0.826 - ETA: 0s - loss: 0.5015 - acc: 0.826 - ETA: 0s - loss: 0.5014 - acc: 0.826 - 97s 867us/step - loss: 0.5013 - acc: 0.8264 - val_loss: 0.4043 - val_acc: 0.8601

Epoch 00006: val_acc improved from 0.83939 to 0.86014, saving model to ./freesound-audio-tagging/models\time.model
Epoch 7/10
 13056/111450 [==>...........................] - ETA: 1:44 - loss: 0.4413 - acc: 0.812 - ETA: 1:37 - loss: 0.4625 - acc: 0.822 - ETA: 1:34 - loss: 0.4097 - acc: 0.843 - ETA: 1:32 - loss: 0.4418 - acc: 0.830 - ETA: 1:31 - loss: 0.4573 - acc: 0.829 - ETA: 1:30 - loss: 0.4778 - acc: 0.829 - ETA: 1:30 - loss: 0.4533 - acc: 0.841 - ETA: 1:30 - loss: 0.4609 - acc: 0.835 - ETA: 1:29 - loss: 0.4651 - acc: 0.831 - ETA: 1:29 - loss: 0.4692 - acc: 0.829 - ETA: 1:29 - loss: 0.4640 - acc: 0.831 - ETA: 1:29 - loss: 0.4739 - acc: 0.828 - ETA: 1:29 - loss: 0.4785 - acc: 0.826 - ETA: 1:29 - loss: 0.4938 - acc: 0.820 - ETA: 1:29 - loss: 0.4929 - acc: 0.820 - ETA: 1:30 - loss: 0.4793 - acc: 0.826 - ETA: 1:30 - loss: 0.4744 - acc: 0.829 - ETA: 1:30 - loss: 0.4707 - acc: 0.831 - ETA: 1:30 - loss: 0.4589 - acc: 0.836 - ETA: 1:30 - loss: 0.4702 - acc: 0.835 - ETA: 1:30 - loss: 0.4701 - acc: 0.837 - ETA: 1:30 - loss: 0.4705 - acc: 0.835 - ETA: 1:30 - loss: 0.4677 - acc: 0.836 - ETA: 1:30 - loss: 0.4658 - acc: 0.835 - 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acc: 0.843 - ETA: 0s - loss: 0.4563 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4561 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4563 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4562 - acc: 0.843 - ETA: 0s - loss: 0.4560 - acc: 0.843 - ETA: 0s - loss: 0.4560 - acc: 0.843 - ETA: 0s - loss: 0.4559 - acc: 0.843 - 98s 876us/step - loss: 0.4559 - acc: 0.8439 - val_loss: 0.3855 - val_acc: 0.8656

Epoch 00007: val_acc improved from 0.86014 to 0.86563, saving model to ./freesound-audio-tagging/models\time.model
Epoch 8/10
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Epoch 00008: val_acc improved from 0.86563 to 0.87492, saving model to ./freesound-audio-tagging/models\time.model
Epoch 9/10
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acc: 0.865 - ETA: 0s - loss: 0.3922 - acc: 0.865 - ETA: 0s - loss: 0.3922 - acc: 0.865 - ETA: 0s - loss: 0.3921 - acc: 0.865 - ETA: 0s - loss: 0.3920 - acc: 0.865 - ETA: 0s - loss: 0.3920 - acc: 0.865 - ETA: 0s - loss: 0.3919 - acc: 0.865 - ETA: 0s - loss: 0.3921 - acc: 0.865 - ETA: 0s - loss: 0.3920 - acc: 0.865 - ETA: 0s - loss: 0.3919 - acc: 0.865 - ETA: 0s - loss: 0.3918 - acc: 0.865 - ETA: 0s - loss: 0.3918 - acc: 0.865 - ETA: 0s - loss: 0.3918 - acc: 0.865 - 97s 868us/step - loss: 0.3918 - acc: 0.8656 - val_loss: 0.3531 - val_acc: 0.8832

Epoch 00009: val_acc improved from 0.87492 to 0.88324, saving model to ./freesound-audio-tagging/models\time.model
Epoch 10/10
 13152/111450 [==>...........................] - ETA: 1:44 - loss: 0.5702 - acc: 0.781 - ETA: 1:28 - loss: 0.4868 - acc: 0.851 - ETA: 1:29 - loss: 0.4637 - acc: 0.833 - ETA: 1:27 - loss: 0.4484 - acc: 0.854 - ETA: 1:26 - loss: 0.4596 - acc: 0.849 - ETA: 1:26 - loss: 0.4337 - acc: 0.854 - ETA: 1:27 - loss: 0.4434 - acc: 0.847 - ETA: 1:27 - loss: 0.4513 - acc: 0.843 - ETA: 1:26 - loss: 0.4462 - acc: 0.846 - ETA: 1:26 - loss: 0.4180 - acc: 0.854 - ETA: 1:27 - loss: 0.4232 - acc: 0.850 - ETA: 1:27 - loss: 0.4255 - acc: 0.848 - ETA: 1:27 - loss: 0.4202 - acc: 0.852 - ETA: 1:28 - loss: 0.4104 - acc: 0.856 - ETA: 1:28 - loss: 0.4034 - acc: 0.858 - ETA: 1:28 - loss: 0.3963 - acc: 0.858 - ETA: 1:28 - loss: 0.3934 - acc: 0.858 - ETA: 1:29 - loss: 0.3895 - acc: 0.859 - ETA: 1:29 - loss: 0.3838 - acc: 0.861 - ETA: 1:29 - loss: 0.3800 - acc: 0.862 - ETA: 1:29 - loss: 0.3794 - acc: 0.864 - ETA: 1:29 - loss: 0.3808 - acc: 0.865 - ETA: 1:29 - loss: 0.3804 - acc: 0.866 - ETA: 1:29 - loss: 0.3860 - acc: 0.866 - 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acc: 0.876 - ETA: 0s - loss: 0.3628 - acc: 0.876 - ETA: 0s - loss: 0.3627 - acc: 0.876 - ETA: 0s - loss: 0.3626 - acc: 0.876 - ETA: 0s - loss: 0.3626 - acc: 0.876 - ETA: 0s - loss: 0.3625 - acc: 0.876 - ETA: 0s - loss: 0.3625 - acc: 0.876 - ETA: 0s - loss: 0.3625 - acc: 0.876 - ETA: 0s - loss: 0.3624 - acc: 0.876 - ETA: 0s - loss: 0.3626 - acc: 0.876 - ETA: 0s - loss: 0.3625 - acc: 0.876 - ETA: 0s - loss: 0.3625 - acc: 0.876 - 98s 876us/step - loss: 0.3624 - acc: 0.8764 - val_loss: 0.3653 - val_acc: 0.8733

Epoch 00010: val_acc did not improve from 0.88324

Predict using RNN Model¶

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'time.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true_r, y_pred_r, fn_prob_r = build_predictions('./freesound-audio-tagging/test_files')
acc_score_r = accuracy_score(y_true = y_true_r, y_pred = y_pred_r)

y_probs_r = []

for i, row in df_t.iterrows():
    y_prob_r = fn_prob_r[row.fname]
    y_probs_r.append(y_prob_r)
    for c, p in zip(classes, y_prob_r):
        df_t.at[i,c] = p

y_pred_r = [classes[np.argmax(y)] for y in y_probs_r]
df_t['y_pred_r'] = y_pred_r

print('RNN Test Accuracy Score: ', acc_score_r)

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

RNN Test Accuracy Score:  0.4111871715966422

3a. Build CNN with 2000 data points¶

df_select.shape

(3000, 3)

df1 = pd.DataFrame()
for c in select_labels:
    #print(c)
    df_stage = df_select[df_select.label == c].sample(frac = 2/3, random_state =1).copy()
    df1 = df1.append(df_stage, ignore_index = True)

df1.shape

(2000, 3)

df1.groupby('label').count()

df2 = df1.set_index('fname')

df2.head()

# Test Data
rem_files = pd.DataFrame(set(df_select.fname.values) - set(df2.index.values))

rd = pd.DataFrame()
for f in rem_files[0]:
    r = df_select[df_select.fname==f]
    rd = rd.append(r)

rd.shape

(1000, 3)

df_t = rd
for f in tqdm_notebook(df_t.fname):
    shutil.copy('./freesound-audio-tagging/clean/'+f, './freesound-audio-tagging/test_files/')
print('Number of test files: ',len(os.listdir('./freesound-audio-tagging/test_files/')))

C:\Users\abhin\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`

Number of test files:  1000

config = Config('conv')

print('Building X, y train data for CNN Model')
X,y = build_rand_feat() # Build feature set from random choices
print('X, y built for train')
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2],1)
print('Building CNN Model')
model = get_conv_model()

Building X, y train data for CNN Model
Dataframe shape (2000, 2)

No. of small files that did not make it to train set: 44
X, y built for train
Building CNN Model
Model: "sequential_19"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_25 (Conv2D)           (None, 9, 13, 16)         160       
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 9, 13, 32)         4640      
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 9, 13, 64)         18496     
_________________________________________________________________
conv2d_28 (Conv2D)           (None, 9, 13, 128)        73856     
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 4, 6, 128)         0         
_________________________________________________________________
dropout_18 (Dropout)         (None, 4, 6, 128)         0         
_________________________________________________________________
flatten_18 (Flatten)         (None, 3072)              0         
_________________________________________________________________
dense_74 (Dense)             (None, 128)               393344    
_________________________________________________________________
dense_75 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_76 (Dense)             (None, 10)                650       
=================================================================
Total params: 499,402
Trainable params: 499,402
Non-trainable params: 0
_________________________________________________________________

Fit the CNN Model with 2000 data points¶

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)
model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 111411 samples, validate on 12379 samples
Epoch 1/10
  6496/111411 [>.............................] - ETA: 16:11 - loss: 2.2914 - acc: 0.15 - ETA: 10:15 - loss: 2.2927 - acc: 0.18 - ETA: 8:11 - loss: 2.3003 - acc: 0.1562 - ETA: 7:12 - loss: 2.2904 - acc: 0.148 - ETA: 6:34 - loss: 2.2858 - acc: 0.143 - ETA: 6:08 - loss: 2.2817 - acc: 0.145 - ETA: 5:49 - loss: 2.2785 - acc: 0.151 - ETA: 5:35 - loss: 2.2863 - acc: 0.168 - ETA: 5:24 - loss: 2.2936 - acc: 0.173 - ETA: 5:14 - loss: 2.2898 - acc: 0.184 - ETA: 5:08 - loss: 2.2842 - acc: 0.190 - ETA: 5:03 - loss: 2.2820 - acc: 0.187 - ETA: 4:58 - loss: 2.2816 - acc: 0.185 - ETA: 4:54 - loss: 2.2766 - acc: 0.185 - ETA: 4:50 - loss: 2.2720 - acc: 0.187 - ETA: 4:47 - loss: 2.2706 - acc: 0.185 - ETA: 4:45 - loss: 2.2654 - acc: 0.189 - ETA: 4:42 - loss: 2.2569 - acc: 0.192 - ETA: 4:40 - loss: 2.2591 - acc: 0.197 - ETA: 4:38 - loss: 2.2530 - acc: 0.200 - ETA: 4:36 - loss: 2.2622 - acc: 0.193 - ETA: 4:34 - loss: 2.2587 - acc: 0.194 - ETA: 4:33 - loss: 2.2579 - acc: 0.195 - ETA: 4:31 - loss: 2.2575 - acc: 0.192 - ETA: 4:30 - loss: 2.2563 - acc: 0.188 - ETA: 4:29 - loss: 2.2538 - acc: 0.189 - ETA: 4:28 - loss: 2.2556 - acc: 0.185 - ETA: 4:27 - loss: 2.2531 - acc: 0.190 - ETA: 4:26 - loss: 2.2542 - acc: 0.190 - ETA: 4:25 - loss: 2.2538 - acc: 0.188 - ETA: 4:24 - loss: 2.2535 - acc: 0.190 - ETA: 4:23 - loss: 2.2523 - acc: 0.190 - ETA: 4:22 - loss: 2.2503 - acc: 0.193 - ETA: 4:21 - loss: 2.2478 - acc: 0.197 - ETA: 4:21 - loss: 2.2456 - acc: 0.200 - ETA: 4:20 - loss: 2.2465 - acc: 0.200 - ETA: 4:19 - loss: 2.2443 - acc: 0.198 - ETA: 4:18 - loss: 2.2385 - acc: 0.199 - ETA: 4:18 - loss: 2.2350 - acc: 0.201 - ETA: 4:17 - loss: 2.2356 - acc: 0.199 - ETA: 4:17 - loss: 2.2315 - acc: 0.199 - ETA: 4:16 - loss: 2.2294 - acc: 0.200 - ETA: 4:16 - loss: 2.2254 - acc: 0.202 - ETA: 4:15 - loss: 2.2236 - acc: 0.200 - ETA: 4:15 - loss: 2.2209 - acc: 0.204 - ETA: 4:14 - loss: 2.2176 - acc: 0.204 - ETA: 4:14 - loss: 2.2154 - acc: 0.205 - ETA: 4:13 - loss: 2.2130 - acc: 0.204 - ETA: 4:13 - loss: 2.2102 - acc: 0.205 - ETA: 4:12 - loss: 2.2063 - acc: 0.206 - ETA: 4:12 - loss: 2.2062 - acc: 0.207 - ETA: 4:12 - loss: 2.2065 - acc: 0.204 - ETA: 4:11 - loss: 2.2042 - acc: 0.205 - ETA: 4:11 - loss: 2.1982 - acc: 0.206 - ETA: 4:10 - loss: 2.1954 - acc: 0.208 - ETA: 4:10 - loss: 2.1914 - acc: 0.208 - ETA: 4:10 - loss: 2.1849 - acc: 0.213 - ETA: 4:10 - loss: 2.1844 - acc: 0.211 - ETA: 4:10 - loss: 2.1815 - acc: 0.211 - ETA: 4:09 - loss: 2.1770 - acc: 0.212 - ETA: 4:09 - loss: 2.1722 - acc: 0.214 - ETA: 4:09 - loss: 2.1673 - acc: 0.217 - ETA: 4:09 - loss: 2.1627 - acc: 0.218 - ETA: 4:09 - loss: 2.1597 - acc: 0.220 - ETA: 4:08 - loss: 2.1538 - acc: 0.223 - ETA: 4:08 - loss: 2.1504 - acc: 0.224 - ETA: 4:08 - loss: 2.1472 - acc: 0.224 - ETA: 4:08 - loss: 2.1381 - acc: 0.225 - ETA: 4:07 - loss: 2.1307 - acc: 0.228 - ETA: 4:07 - loss: 2.1266 - acc: 0.229 - ETA: 4:07 - loss: 2.1229 - acc: 0.229 - ETA: 4:07 - loss: 2.1184 - acc: 0.233 - ETA: 4:07 - loss: 2.1140 - acc: 0.236 - ETA: 4:08 - loss: 2.1129 - acc: 0.236 - ETA: 4:09 - loss: 2.1128 - acc: 0.236 - ETA: 4:10 - loss: 2.1049 - acc: 0.240 - ETA: 4:10 - loss: 2.1017 - acc: 0.240 - ETA: 4:10 - loss: 2.1001 - acc: 0.240 - ETA: 4:10 - loss: 2.0957 - acc: 0.242 - ETA: 4:11 - loss: 2.0938 - acc: 0.242 - ETA: 4:12 - loss: 2.0912 - acc: 0.244 - ETA: 4:13 - loss: 2.0875 - acc: 0.245 - ETA: 4:13 - loss: 2.0837 - acc: 0.246 - ETA: 4:13 - loss: 2.0775 - acc: 0.248 - ETA: 4:15 - loss: 2.0723 - acc: 0.249 - ETA: 4:15 - loss: 2.0668 - acc: 0.251 - ETA: 4:14 - loss: 2.0658 - acc: 0.253 - ETA: 4:14 - loss: 2.0634 - acc: 0.253 - ETA: 4:14 - loss: 2.0621 - acc: 0.253 - ETA: 4:14 - loss: 2.0577 - acc: 0.254 - ETA: 4:14 - loss: 2.0565 - acc: 0.254 - ETA: 4:15 - loss: 2.0568 - acc: 0.254 - ETA: 4:15 - loss: 2.0522 - acc: 0.255 - ETA: 4:15 - loss: 2.0493 - acc: 0.257 - ETA: 4:15 - loss: 2.0453 - acc: 0.258 - ETA: 4:16 - loss: 2.0426 - acc: 0.259 - ETA: 4:17 - loss: 2.0407 - acc: 0.260 - ETA: 4:17 - loss: 2.0392 - acc: 0.261 - ETA: 4:16 - loss: 2.0357 - acc: 0.261 - ETA: 4:16 - loss: 2.0329 - acc: 0.262 - ETA: 4:16 - loss: 2.0311 - acc: 0.263 - ETA: 4:15 - loss: 2.0311 - acc: 0.263 - ETA: 4:15 - loss: 2.0268 - acc: 0.265 - ETA: 4:16 - loss: 2.0267 - acc: 0.265 - ETA: 4:17 - loss: 2.0234 - acc: 0.266 - ETA: 4:17 - loss: 2.0218 - acc: 0.266 - ETA: 4:16 - loss: 2.0208 - acc: 0.266 - ETA: 4:16 - loss: 2.0177 - acc: 0.266 - ETA: 4:17 - loss: 2.0174 - acc: 0.266 - ETA: 4:18 - loss: 2.0162 - acc: 0.266 - ETA: 4:19 - loss: 2.0136 - acc: 0.266 - ETA: 4:20 - loss: 2.0089 - acc: 0.267 - ETA: 4:20 - loss: 2.0073 - acc: 0.268 - ETA: 4:20 - loss: 2.0049 - acc: 0.270 - ETA: 4:19 - loss: 2.0038 - acc: 0.270 - ETA: 4:19 - loss: 2.0009 - acc: 0.270 - ETA: 4:18 - loss: 1.9987 - acc: 0.271 - ETA: 4:18 - loss: 1.9973 - acc: 0.270 - ETA: 4:18 - loss: 1.9951 - acc: 0.271 - ETA: 4:18 - loss: 1.9914 - acc: 0.272 - ETA: 4:17 - loss: 1.9893 - acc: 0.273 - ETA: 4:17 - loss: 1.9860 - acc: 0.274 - ETA: 4:17 - loss: 1.9847 - acc: 0.274 - ETA: 4:16 - loss: 1.9815 - acc: 0.276 - ETA: 4:16 - loss: 1.9805 - acc: 0.276 - ETA: 4:16 - loss: 1.9789 - acc: 0.276 - ETA: 4:16 - loss: 1.9775 - acc: 0.277 - ETA: 4:15 - loss: 1.9759 - acc: 0.278 - ETA: 4:15 - loss: 1.9757 - acc: 0.278 - ETA: 4:15 - loss: 1.9727 - acc: 0.279 - ETA: 4:15 - loss: 1.9693 - acc: 0.280 - ETA: 4:14 - loss: 1.9673 - acc: 0.282 - ETA: 4:14 - loss: 1.9646 - acc: 0.283 - ETA: 4:14 - loss: 1.9659 - acc: 0.283 - ETA: 4:14 - loss: 1.9638 - acc: 0.284 - ETA: 4:14 - loss: 1.9620 - acc: 0.285 - ETA: 4:13 - loss: 1.9607 - acc: 0.285 - ETA: 4:13 - loss: 1.9587 - acc: 0.286 - ETA: 4:13 - loss: 1.9560 - acc: 0.288 - ETA: 4:13 - loss: 1.9535 - acc: 0.289 - ETA: 4:12 - loss: 1.9522 - acc: 0.290 - ETA: 4:12 - loss: 1.9507 - acc: 0.291 - ETA: 4:12 - loss: 1.9503 - acc: 0.292 - ETA: 4:12 - loss: 1.9488 - acc: 0.293 - ETA: 4:11 - loss: 1.9454 - acc: 0.294 - ETA: 4:11 - loss: 1.9433 - acc: 0.294 - ETA: 4:11 - loss: 1.9413 - acc: 0.295 - ETA: 4:12 - loss: 1.9378 - acc: 0.298 - ETA: 4:12 - loss: 1.9364 - acc: 0.298 - ETA: 4:12 - loss: 1.9344 - acc: 0.299 - ETA: 4:12 - loss: 1.9321 - acc: 0.299 - ETA: 4:12 - loss: 1.9298 - acc: 0.300 - ETA: 4:12 - loss: 1.9280 - acc: 0.300 - ETA: 4:11 - loss: 1.9262 - acc: 0.301 - ETA: 4:11 - loss: 1.9228 - acc: 0.302 - ETA: 4:11 - loss: 1.9198 - acc: 0.303 - ETA: 4:11 - loss: 1.9167 - acc: 0.304 - ETA: 4:10 - loss: 1.9155 - acc: 0.305 - ETA: 4:10 - loss: 1.9152 - acc: 0.305 - ETA: 4:10 - loss: 1.9137 - acc: 0.305 - ETA: 4:10 - loss: 1.9110 - acc: 0.306 - ETA: 4:09 - loss: 1.9093 - acc: 0.306 - ETA: 4:09 - loss: 1.9073 - acc: 0.306 - ETA: 4:09 - loss: 1.9051 - acc: 0.307 - ETA: 4:09 - loss: 1.9020 - acc: 0.308 - ETA: 4:09 - loss: 1.8997 - acc: 0.309 - ETA: 4:08 - loss: 1.8983 - acc: 0.310 - ETA: 4:08 - loss: 1.8964 - acc: 0.311 - ETA: 4:08 - loss: 1.8970 - acc: 0.310 - ETA: 4:08 - loss: 1.8932 - acc: 0.312 - ETA: 4:08 - loss: 1.8928 - acc: 0.312 - ETA: 4:07 - loss: 1.8931 - acc: 0.312 - ETA: 4:07 - loss: 1.8927 - acc: 0.312 - ETA: 4:07 - loss: 1.8907 - acc: 0.313 - ETA: 4:07 - loss: 1.8896 - acc: 0.313 - ETA: 4:07 - loss: 1.8896 - acc: 0.313 - ETA: 4:06 - loss: 1.8878 - acc: 0.313 - ETA: 4:06 - loss: 1.8867 - acc: 0.313 - ETA: 4:06 - loss: 1.8859 - acc: 0.313 - ETA: 4:06 - loss: 1.8850 - acc: 0.313 - ETA: 4:06 - loss: 1.8838 - acc: 0.313 - ETA: 4:05 - loss: 1.8826 - acc: 0.314 - ETA: 4:05 - loss: 1.8816 - acc: 0.315 - ETA: 4:05 - loss: 1.8815 - acc: 0.315 - ETA: 4:05 - loss: 1.8784 - acc: 0.316 - ETA: 4:05 - loss: 1.8763 - acc: 0.317 - ETA: 4:04 - loss: 1.8764 - acc: 0.317 - ETA: 4:04 - loss: 1.8749 - acc: 0.318 - ETA: 4:04 - loss: 1.8728 - acc: 0.318 - ETA: 4:04 - loss: 1.8695 - acc: 0.319 - ETA: 4:04 - loss: 1.8681 - acc: 0.320 - ETA: 4:04 - loss: 1.8668 - acc: 0.320 - ETA: 4:03 - loss: 1.8646 - acc: 0.321 - ETA: 4:03 - loss: 1.8655 - acc: 0.321 - ETA: 4:03 - loss: 1.8629 - acc: 0.322 - ETA: 4:03 - loss: 1.8613 - acc: 0.322 - ETA: 4:03 - loss: 1.8589 - acc: 0.324 - ETA: 4:03 - loss: 1.8570 - acc: 0.324 - ETA: 4:02 - loss: 1.8558 - acc: 0.324 - 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251s 2ms/step - loss: 1.1169 - acc: 0.6061 - val_loss: 0.8194 - val_acc: 0.7110

Epoch 00001: val_acc improved from -inf to 0.71096, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 2/10
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val_loss: 0.6584 - val_acc: 0.7761

Epoch 00002: val_acc improved from 0.71096 to 0.77607, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 3/10
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ETA: 0s - loss: 0.6582 - acc: 0.773 - 259s 2ms/step - loss: 0.6582 - acc: 0.7735 - val_loss: 0.5869 - val_acc: 0.8017

Epoch 00003: val_acc improved from 0.77607 to 0.80168, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 4/10
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257s 2ms/step - loss: 0.5849 - acc: 0.7990 - val_loss: 0.4955 - val_acc: 0.8314

Epoch 00004: val_acc improved from 0.80168 to 0.83141, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 5/10
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Epoch 00005: val_acc improved from 0.83141 to 0.84934, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 6/10
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256s 2ms/step - loss: 0.4969 - acc: 0.8293 - val_loss: 0.4661 - val_acc: 0.8437

Epoch 00006: val_acc did not improve from 0.84934
Epoch 7/10
  6528/111411 [>.............................] - ETA: 4:34 - loss: 0.4640 - acc: 0.843 - ETA: 4:11 - loss: 0.5860 - acc: 0.781 - ETA: 4:04 - loss: 0.5494 - acc: 0.802 - ETA: 4:00 - loss: 0.5354 - acc: 0.812 - ETA: 4:02 - loss: 0.5651 - acc: 0.800 - ETA: 4:00 - loss: 0.5145 - acc: 0.822 - ETA: 4:00 - loss: 0.4805 - acc: 0.839 - ETA: 3:59 - loss: 0.4777 - acc: 0.839 - ETA: 4:00 - loss: 0.4882 - acc: 0.829 - ETA: 3:59 - loss: 0.5077 - acc: 0.825 - ETA: 3:59 - loss: 0.5153 - acc: 0.818 - ETA: 3:59 - loss: 0.5260 - acc: 0.815 - ETA: 3:58 - loss: 0.5200 - acc: 0.822 - ETA: 3:58 - loss: 0.5090 - acc: 0.825 - ETA: 3:58 - loss: 0.5102 - acc: 0.820 - ETA: 3:58 - loss: 0.4965 - acc: 0.824 - ETA: 3:57 - loss: 0.5031 - acc: 0.823 - ETA: 3:57 - loss: 0.5163 - acc: 0.816 - ETA: 3:58 - loss: 0.5089 - acc: 0.819 - ETA: 3:58 - loss: 0.5109 - acc: 0.814 - ETA: 3:58 - loss: 0.5104 - acc: 0.814 - ETA: 3:57 - loss: 0.5164 - acc: 0.812 - ETA: 3:58 - loss: 0.5160 - acc: 0.813 - ETA: 3:58 - loss: 0.5109 - acc: 0.815 - 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Epoch 00007: val_acc improved from 0.84934 to 0.86340, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 8/10
  6528/111411 [>.............................] - ETA: 3:59 - loss: 0.4985 - acc: 0.812 - ETA: 3:52 - loss: 0.5930 - acc: 0.781 - ETA: 3:54 - loss: 0.5684 - acc: 0.791 - ETA: 3:54 - loss: 0.5945 - acc: 0.765 - ETA: 3:53 - loss: 0.5574 - acc: 0.787 - ETA: 3:52 - loss: 0.5463 - acc: 0.807 - ETA: 3:53 - loss: 0.4993 - acc: 0.825 - ETA: 3:54 - loss: 0.5439 - acc: 0.820 - ETA: 3:53 - loss: 0.5142 - acc: 0.826 - ETA: 3:53 - loss: 0.5107 - acc: 0.821 - ETA: 3:56 - loss: 0.5266 - acc: 0.812 - ETA: 3:56 - loss: 0.5255 - acc: 0.817 - ETA: 3:56 - loss: 0.5186 - acc: 0.817 - ETA: 3:55 - loss: 0.5034 - acc: 0.823 - ETA: 3:56 - loss: 0.4937 - acc: 0.825 - ETA: 3:55 - loss: 0.4925 - acc: 0.826 - ETA: 3:55 - loss: 0.4889 - acc: 0.827 - ETA: 3:55 - loss: 0.4787 - acc: 0.831 - ETA: 3:55 - loss: 0.4723 - acc: 0.832 - ETA: 3:55 - loss: 0.4911 - acc: 0.832 - ETA: 3:55 - loss: 0.4986 - acc: 0.831 - ETA: 3:54 - loss: 0.4968 - acc: 0.831 - ETA: 3:54 - loss: 0.4848 - acc: 0.834 - ETA: 3:54 - loss: 0.4811 - acc: 0.834 - 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254s 2ms/step - loss: 0.4299 - acc: 0.8521 - val_loss: 0.3897 - val_acc: 0.8708

Epoch 00008: val_acc improved from 0.86340 to 0.87083, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 9/10
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Epoch 00009: val_acc improved from 0.87083 to 0.88254, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 10/10
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ETA: 0s - loss: 0.3902 - acc: 0.865 - 258s 2ms/step - loss: 0.3901 - acc: 0.8654 - val_loss: 0.3548 - val_acc: 0.8813

Epoch 00010: val_acc did not improve from 0.88254

val_acc did not improve from 0.88254

Predicting using CNN Model on remaining 1000 datapoints¶

df_t.shape

(1000, 3)

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'conv.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true, y_pred, fn_prob = build_predictions('./freesound-audio-tagging/test_files')
acc_score = accuracy_score(y_true = y_true, y_pred = y_pred)

y_probs = []

for i, row in df_t.iterrows():
    y_prob = fn_prob[row.fname]
    y_probs.append(y_prob)
    for c, p in zip(classes, y_prob):
        df_t.at[i,c] = p

y_pred = [classes[np.argmax(y)] for y in y_probs]
df_t['y_pred'] = y_pred

print('Test Accuracy Score', acc_score)

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

Test Accuracy Score 0.6075326788112668

3b. Build RNN Model on 2000 audio files¶

config = Config(mode='time')
print('Building RNN Model')
X,y = build_rand_feat()
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2])
print('Retrieving RNN Model')
model = get_recurrent_model()

Building RNN Model
Loading existing data for time model
Dataframe shape (2000, 3)
<__main__.Config object at 0x0000026B1CED7160>
Retrieving RNN Model
Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_3 (LSTM)                (None, 9, 128)            72704     
_________________________________________________________________
lstm_4 (LSTM)                (None, 9, 128)            131584    
_________________________________________________________________
dropout_5 (Dropout)          (None, 9, 128)            0         
_________________________________________________________________
time_distributed_5 (TimeDist (None, 9, 64)             8256      
_________________________________________________________________
time_distributed_6 (TimeDist (None, 9, 32)             2080      
_________________________________________________________________
time_distributed_7 (TimeDist (None, 9, 16)             528       
_________________________________________________________________
time_distributed_8 (TimeDist (None, 9, 8)              136       
_________________________________________________________________
flatten_5 (Flatten)          (None, 72)                0         
_________________________________________________________________
dense_19 (Dense)             (None, 10)                730       
=================================================================
Total params: 216,018
Trainable params: 216,018
Non-trainable params: 0
_________________________________________________________________

Fit Model¶

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)
model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.1,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 214925 samples, validate on 23881 samples
Epoch 1/10
 22528/214925 [==>...........................] - ETA: 4:11:13 - loss: 2.2907 - acc: 0.15 - ETA: 51:26 - loss: 2.2803 - acc: 0.1437 - ETA: 29:14 - loss: 2.2700 - acc: 0.15 - ETA: 22:24 - loss: 2.2537 - acc: 0.17 - ETA: 17:09 - loss: 2.2391 - acc: 0.18 - ETA: 14:03 - loss: 2.2459 - acc: 0.18 - ETA: 11:57 - loss: 2.2368 - acc: 0.19 - ETA: 10:27 - loss: 2.2342 - acc: 0.19 - ETA: 9:19 - loss: 2.2247 - acc: 0.1943 - ETA: 8:27 - loss: 2.2216 - acc: 0.194 - ETA: 7:46 - loss: 2.2218 - acc: 0.189 - ETA: 7:11 - loss: 2.2253 - acc: 0.187 - ETA: 6:42 - loss: 2.2230 - acc: 0.188 - ETA: 6:18 - loss: 2.2190 - acc: 0.191 - ETA: 5:57 - loss: 2.2128 - acc: 0.193 - ETA: 5:39 - loss: 2.2213 - acc: 0.190 - ETA: 5:23 - loss: 2.2196 - acc: 0.189 - ETA: 5:09 - loss: 2.2239 - acc: 0.184 - ETA: 4:57 - loss: 2.2242 - acc: 0.185 - ETA: 4:46 - loss: 2.2234 - acc: 0.185 - ETA: 4:36 - loss: 2.2219 - acc: 0.185 - ETA: 4:27 - loss: 2.2230 - acc: 0.185 - ETA: 4:19 - loss: 2.2208 - acc: 0.184 - ETA: 4:12 - loss: 2.2232 - 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Epoch 00001: val_acc improved from -inf to 0.59704, saving model to ./freesound-audio-tagging/models\time.model
Epoch 2/10
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acc: 0.622 - ETA: 0s - loss: 1.0789 - acc: 0.622 - ETA: 0s - loss: 1.0789 - acc: 0.622 - ETA: 0s - loss: 1.0788 - acc: 0.622 - ETA: 0s - loss: 1.0787 - acc: 0.622 - ETA: 0s - loss: 1.0786 - acc: 0.622 - ETA: 0s - loss: 1.0786 - acc: 0.622 - ETA: 0s - loss: 1.0785 - acc: 0.622 - ETA: 0s - loss: 1.0785 - acc: 0.622 - ETA: 0s - loss: 1.0786 - acc: 0.622 - ETA: 0s - loss: 1.0786 - acc: 0.622 - 114s 531us/step - loss: 1.0786 - acc: 0.6223 - val_loss: 0.9358 - val_acc: 0.6767

Epoch 00002: val_acc improved from 0.59704 to 0.67673, saving model to ./freesound-audio-tagging/models\time.model
Epoch 3/10
 24608/214925 [==>...........................] - ETA: 2:13 - loss: 0.7167 - acc: 0.718 - ETA: 1:43 - loss: 0.9023 - acc: 0.700 - ETA: 1:38 - loss: 0.9218 - acc: 0.691 - ETA: 1:42 - loss: 0.8832 - acc: 0.705 - ETA: 1:40 - loss: 0.9196 - acc: 0.681 - ETA: 1:40 - loss: 0.9224 - acc: 0.689 - ETA: 1:39 - loss: 0.9464 - acc: 0.678 - ETA: 1:40 - loss: 0.9599 - acc: 0.674 - ETA: 1:40 - loss: 0.9633 - acc: 0.673 - ETA: 1:39 - loss: 0.9866 - acc: 0.670 - ETA: 1:39 - loss: 0.9682 - acc: 0.677 - ETA: 1:38 - loss: 0.9620 - acc: 0.673 - ETA: 1:38 - loss: 0.9450 - acc: 0.678 - ETA: 1:38 - loss: 0.9370 - acc: 0.676 - ETA: 1:38 - loss: 0.9413 - acc: 0.674 - ETA: 1:38 - loss: 0.9445 - acc: 0.674 - ETA: 1:37 - loss: 0.9387 - acc: 0.671 - ETA: 1:37 - loss: 0.9406 - acc: 0.672 - ETA: 1:37 - loss: 0.9421 - acc: 0.671 - ETA: 1:37 - loss: 0.9416 - acc: 0.673 - ETA: 1:37 - loss: 0.9469 - acc: 0.670 - ETA: 1:37 - loss: 0.9572 - acc: 0.668 - ETA: 1:37 - loss: 0.9573 - acc: 0.668 - ETA: 1:37 - loss: 0.9567 - acc: 0.669 - 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Epoch 00003: val_acc improved from 0.67673 to 0.70131, saving model to ./freesound-audio-tagging/models\time.model
Epoch 4/10
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Epoch 00004: val_acc improved from 0.70131 to 0.75131, saving model to ./freesound-audio-tagging/models\time.model
Epoch 5/10
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Epoch 00005: val_acc improved from 0.75131 to 0.76793, saving model to ./freesound-audio-tagging/models\time.model
Epoch 6/10
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Epoch 00006: val_acc improved from 0.76793 to 0.79590, saving model to ./freesound-audio-tagging/models\time.model
Epoch 7/10
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val_loss: 0.5462 - val_acc: 0.8153

Epoch 00007: val_acc improved from 0.79590 to 0.81533, saving model to ./freesound-audio-tagging/models\time.model
Epoch 8/10
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Epoch 00008: val_acc did not improve from 0.81533
Epoch 9/10
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Epoch 00009: val_acc did not improve from 0.81533
Epoch 10/10
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Epoch 00010: val_acc did not improve from 0.81533

val_acc improved from 0.75495 to 0.75697

Predict using RNN Model on 1000 files¶

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'time.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true_r, y_pred_r, fn_prob_r = build_predictions('./freesound-audio-tagging/test_files')
acc_score_r = accuracy_score(y_true = y_true_r, y_pred = y_pred_r)

y_probs_r = []

for i, row in df_t.iterrows():
    y_prob_r = fn_prob_r[row.fname]
    y_probs_r.append(y_prob_r)
    for c, p in zip(classes, y_prob_r):
        df_t.at[i,c] = p

y_pred_r = [classes[np.argmax(y)] for y in y_probs_r]
df_t['y_pred_r'] = y_pred_r

print('RNN Test Accuracy Score: ', acc_score_r)

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

RNN Test Accuracy Score:  0.5661237538543026

4. Building CNN model from MobileNetV2¶

config = Config('conv')

print('Building X, y train data for CNN Model')
X,y = build_rand_feat() # Build feature set from random choices
print('X, y built for train')
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2],1)
print('Building CNN Model')

Building X, y train data for CNN Model
Loading existing data for conv model
Dataframe shape (2000, 3)
<__main__.Config object at 0x0000012E517EDFD0>
X, y built for train
Building CNN Model

model = get_conv_model_MobileNetv2()

5. Building CNN Model 2¶

config = Config('conv')

print('Building X, y train data for CNN Model 2')
X,y = build_rand_feat() # Build feature set from random choices
print('X, y built for train')
y_flat = np.argmax(y, axis = 1)
input_shape = (X.shape[1], X.shape[2],1)
print('Building CNN Model')

Building X, y train data for CNN Model 2
Loading existing data for conv model
Dataframe shape (2000, 3)
<__main__.Config object at 0x0000012E6D52D240>
X, y built for train
Building CNN Model

model = get_conv_model2()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_13 (Conv2D)           (None, 9, 13, 16)         160       
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 9, 13, 32)         4640      
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 9, 13, 64)         18496     
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 9, 13, 128)        73856     
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 9, 13, 256)        295168    
_________________________________________________________________
dropout_4 (Dropout)          (None, 9, 13, 256)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 4, 6, 256)         0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 4, 6, 256)         0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 6144)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 256)               1573120   
_________________________________________________________________
dense_10 (Dense)             (None, 128)               32896     
_________________________________________________________________
dense_11 (Dense)             (None, 64)                8256      
_________________________________________________________________
dense_12 (Dense)             (None, 10)                650       
=================================================================
Total params: 2,007,242
Trainable params: 2,007,242
Non-trainable params: 0
_________________________________________________________________

df_select.shape

(3000, 3)

Fit Model¶

class_weight = compute_class_weight('balanced',
                                  np.unique(y_flat),
                                  y_flat )

checkpoint = ModelCheckpoint(config.model_path, monitor = 'val_acc', 
                             verbose = 1, mode = 'max', save_best_only=True,
                            save_weights_only=False, period =1)

model.fit(X,y, epochs = 10, batch_size = 32, shuffle = True, 
         class_weight = class_weight, validation_split=0.2,
         callbacks=[checkpoint]
         ) 
model.save(config.model_path)

Train on 191040 samples, validate on 47761 samples
Epoch 1/10
191040/191040 [==============================] - 741s 4ms/step - loss: 0.9500 - acc: 0.6666 - val_loss: 0.6299 - val_acc: 0.7892

Epoch 00001: val_acc improved from -inf to 0.78922, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 2/10
191040/191040 [==============================] - 762s 4ms/step - loss: 0.5577 - acc: 0.8100 - val_loss: 0.4606 - val_acc: 0.8481

Epoch 00002: val_acc improved from 0.78922 to 0.84812, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 3/10
191040/191040 [==============================] - 780s 4ms/step - loss: 0.4378 - acc: 0.8511 - val_loss: 0.3777 - val_acc: 0.8772

Epoch 00003: val_acc improved from 0.84812 to 0.87722, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 4/10
191040/191040 [==============================] - 779s 4ms/step - loss: 0.3758 - acc: 0.8723 - val_loss: 0.3468 - val_acc: 0.8857

Epoch 00004: val_acc improved from 0.87722 to 0.88570, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 5/10
191040/191040 [==============================] - 781s 4ms/step - loss: 0.3328 - acc: 0.8862 - val_loss: 0.2919 - val_acc: 0.9065

Epoch 00005: val_acc improved from 0.88570 to 0.90647, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 6/10
191040/191040 [==============================] - 782s 4ms/step - loss: 0.3018 - acc: 0.8977 - val_loss: 0.2696 - val_acc: 0.9122

Epoch 00006: val_acc improved from 0.90647 to 0.91223, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 7/10
191040/191040 [==============================] - 774s 4ms/step - loss: 0.2784 - acc: 0.9055 - val_loss: 0.2787 - val_acc: 0.9125

Epoch 00007: val_acc improved from 0.91223 to 0.91250, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 8/10
191040/191040 [==============================] - 776s 4ms/step - loss: 0.2579 - acc: 0.9132 - val_loss: 0.2640 - val_acc: 0.9152

Epoch 00008: val_acc improved from 0.91250 to 0.91520, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 9/10
191040/191040 [==============================] - 805s 4ms/step - loss: 0.2446 - acc: 0.9180 - val_loss: 0.2484 - val_acc: 0.9195

Epoch 00009: val_acc improved from 0.91520 to 0.91949, saving model to ./freesound-audio-tagging/models\conv.model
Epoch 10/10
191040/191040 [==============================] - 777s 4ms/step - loss: 0.2306 - acc: 0.9226 - val_loss: 0.2360 - val_acc: 0.9251

Epoch 00010: val_acc improved from 0.91949 to 0.92509, saving model to ./freesound-audio-tagging/models\conv.model

Predict on test¶

classes = list(np.unique(df_t.label))
fn2class = dict(zip(df_t.fname, df_t.label))
p_path = os.path.join('./freesound-audio-tagging/pickles', 'conv.p')

with open(p_path, 'rb') as handle:
    config = pickle.load(handle)
    
# get min and max
model = load_model(config.model_path)
y_true, y_pred, fn_prob = build_predictions('./freesound-audio-tagging/test_files')
acc_score = accuracy_score(y_true = y_true, y_pred = y_pred)

y_probs = []

for i, row in df_t.iterrows():
    y_prob = fn_prob[row.fname]
    y_probs.append(y_prob)
    for c, p in zip(classes, y_prob):
        df_t.at[i,c] = p

y_pred = [classes[np.argmax(y)] for y in y_probs]
df_t['y_pred'] = y_pred

print('Test Accuracy Score', acc_score)

Extracting features from audio

C:\Users\abhin\Anaconda3\lib\site-packages\ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
  import sys

C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
C:\Users\abhin\Anaconda3\lib\site-packages\numpy\core\_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)

Test Accuracy Score 0.6187503546904262

Summary¶

In summary we find that RNNs didn't do a great job and always lagged behind in accuracy. Since the computation was increasing with increasing data size, there was not much motivation to use it till the 5th attempt.

With CNN Models - conv1 and conv2, we found that increasing layers in the hope of getting more features just did not help and rather resulted in overfitting the model. We also used L2 regularization, Dropouts and balanced datasets to have a more approrpiate dataset for training.

Image(filename='./freesound-audio-tagging/Performance Summary.jpg')

Finally, we used pretrained MobileNetV2 model which is a lighter version of Imagenet. Although we achieved 3 points of better accuracy, we couldn't find anything better than that even after increasing epochs from 10 to 25.

As per 21st Mar 2019, MobileNetV2 has achieved a maximum accuracy of 72% - 75%.

Source: https://towardsdatascience.com/review-mobilenetv2-light-weight-model-image-classification-8febb490e61c

Image(filename='./freesound-audio-tagging/MobileNetv2 performance.jpg')

	fname	label	manually_verified
0	00044347.wav	Hi-hat	0
1	001ca53d.wav	Saxophone	1
2	002d256b.wav	Trumpet	0
3	0033e230.wav	Glockenspiel	1
4	00353774.wav	Cello	1

	fname	label	manually_verified	time_series	nframes
0	a4c6a3f7.wav	Acoustic_guitar	1	[75, 74, 71, 67, 68, 65, 66, 62, 58, 57, 56, 5...	1077804
1	6a446a35.wav	Acoustic_guitar	0	[4, 2, -2, 1, -1, 1, 6, 0, 9, 5, 3, 5, 0, 15, ...	342216
2	a18de0a5.wav	Acoustic_guitar	1	[0, -1, 0, -3, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2...	29988
3	b8279dc3.wav	Acoustic_guitar	0	[-62, 92, -110, -383, -397, -471, -483, -591, ...	53802
4	5b40b38c.wav	Acoustic_guitar	0	[-2, 2, -1, -1, 0, 1, 0, 1, -4, 1, -2, 0, -1, ...	319284

	fname	label	manually_verified	Acoustic_guitar	Bass_drum	Cello	Clarinet	Double_bass	Flute	Hi-hat	Saxophone	Snare_drum	Violin_or_fiddle	y_pred	y_pred_r
5166	8c452b56.wav	Flute	1	0.004766	0.002135	0.021029	0.056263	0.000340	0.817588	0.003660	0.058296	0.003201	0.032722	Flute	Flute
4073	6dd8c59b.wav	Violin_or_fiddle	1	0.119150	0.001936	0.022583	0.002844	0.001701	0.030531	0.007123	0.032881	0.005658	0.775593	Violin_or_fiddle	Violin_or_fiddle
1163	206a5cb4.wav	Snare_drum	0	0.379675	0.066067	0.055417	0.002986	0.041933	0.031688	0.022587	0.017181	0.341928	0.040538	Snare_drum	Acoustic_guitar
1355	254d9054.wav	Snare_drum	1	0.014108	0.027139	0.000212	0.000375	0.000038	0.000752	0.063628	0.000744	0.892482	0.000522	Snare_drum	Snare_drum
614	118831d9.wav	Cello	0	0.469527	0.029081	0.337856	0.001325	0.091962	0.013854	0.001135	0.006611	0.013865	0.034783	Cello	Acoustic_guitar

	fname	manually_verified
label
Acoustic_guitar	300	300
Applause	300	300
Bark	239	239
Bass_drum	300	300
Burping_or_eructation	210	210

	fname	label	manually_verified	length
0	a4c6a3f7.wav	Acoustic_guitar	1	24.44
1	6a446a35.wav	Acoustic_guitar	0	7.76
2	a18de0a5.wav	Acoustic_guitar	1	0.68
3	b8279dc3.wav	Acoustic_guitar	0	1.22
4	5b40b38c.wav	Acoustic_guitar	0	7.24

	fname	label	manually_verified
6517	b235e178.wav	Clarinet	0
7569	cd939e21.wav	Saxophone	1
25	00d40fa2.wav	Snare_drum	0
4937	853c5a08.wav	Cello	1
3346	5a55b3dd.wav	Clarinet	0

	fname	label
1353	253b91fe.wav	Flute
7377	c84df40e.wav	Double_bass
6356	ae0820a8.wav	Double_bass
173	0513cbfc.wav	Bass_drum
7916	d6b32ecc.wav	Bass_drum

	fname	manually_verified
label
Acoustic_guitar	200	200
Bass_drum	200	200
Cello	200	200
Clarinet	200	200
Double_bass	200	200
Flute	200	200
Hi-hat	200	200
Saxophone	200	200
Snare_drum	200	200
Violin_or_fiddle	200	200

	fname	manually_verified
label
Acoustic_guitar	200	200
Bass_drum	200	200
Cello	200	200
Clarinet	200	200
Double_bass	200	200
Flute	200	200
Hi-hat	200	200
Saxophone	200	200
Snare_drum	200	200
Violin_or_fiddle	200	200